System and method for data analytics and visualization

ABSTRACT

Systems and methods are described that provide a dynamic reporting functionality that can identify important information and dynamically present a report about the important information that highlights important findings to the user. The described systems and methods are generally described in the field of diabetes management, but are applicable to other medical reports as well. In one implementation, the dynamic reports are based on available data and devices. For example, useless sections of the report, such as those with no populated data, may be removed, minimized in importance, assigned a lower priority, or the like.

INCORPORATION BY REFERENCE TO RELATED APPLICATION

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application claims the benefit of U.S. ProvisionalApplication No. 62/060,351 filed Oct. 6, 2014. The aforementionedapplication is incorporated by reference herein in its entirety, and ishereby expressly made a part of this specification.

FIELD

The present disclosure generally relates to data processing of medicalmeasurements of a host, and in particular ways to present such data.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin, such as in the case of Type I diabetes and/or inwhich insulin is not effective, such as Type 2 diabetes. In a diabeticstate, a victim suffers from high blood sugar, which causes an array ofphysiological derangements, such as kidney failure, skin ulcers, orbleeding into the vitreous of the eye, associated with the deteriorationof small blood vessels. A hypoglycemic reaction, such as low bloodsugar, may be induced by an inadvertent overdose of insulin, or after anormal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

A diabetic person may carry a self-monitoring blood glucose (SMBG)monitor, which typically requires uncomfortable finger pricking methods.Due to the lack of comfort and convenience, a diabetic typicallymeasures his or her glucose level only two to four times per day.Unfortunately, these time intervals are spread so far apart that thediabetic will likely find out too late, sometimes incurring dangerousside effects, of a hyperglycemic or hypoglycemic condition. In fact, itis not only unlikely that a diabetic will take a timely SMBG value, butadditionally the diabetic will not know if his blood glucose value ishigher or lower based on conventional methods.

Consequently, a variety of non-invasive, transdermal (e.g.,transcutaneous) and/or implantable electrochemical sensors are beingdeveloped for continuously detecting and/or quantifying blood glucosevalues. These devices generally transmit raw or minimally processed datafor subsequent analysis at a remote device, which can include a display,to allow presentation of information to a user hosting the sensor.

Using such systems, glucose values can be immediately displayed to theuser. Data from such sensors can also be transmitted to a remotelocation, and compiled into one or more reports. One problem with suchreports is that the same typically have a set report format. The varioussections and sub-sections of the report remain the same, whether thereis sufficient data to make the section relevant to the user or not. Forexample, there may not be any insulin data available to generate thereport, but the report may have sections that deal with insulin dataanyway. This can make the report bulkier, and less comprehensible to theuser.

In the same way, static reports can result in presenting information ina way in which important information is either not presented to the userand not presented in a user friendly way. As an example, importantrecognized patterns could be buried in a report because of the setformat. Not only could a user expend considerable time and effort inrecognizing the information, but a user could miss the importantinformation altogether.

Moreover, prior art data reporting systems are typically set up with aparticular user in mind. This can limit the usefulness of the system tothe particular type of person for which the system was designed. Forexample, the system may be set up for a specialized doctor (e.g.endocrinologist), in which case the system may be designed to provideabundance of detail. However, a new patient may not be able toadequately use the system. Conversely, a simple system that could beuseful for new patients may not provide the detail desired by thespecialized professional.

In addition, prior art reports tend to follow old conventions that arenot necessarily intuitive, and may be difficult to manage efficientlyduring a doctor-patient visit. The doctor may have to flip around thereport while discussing the report with the patient, because informationis not provided in a convenient way. Finally, the report may contain alot of details not relevant to the doctor-patient conversation.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY

Systems and methods according to present principles provide a dynamicreporting functionality that can identify important information anddynamically present a report about the important information thathighlights important findings to the user. Systems and methods aregenerally described in the field of diabetes management, but areapplicable to other medical reports as well.

In one aspect, the systems and methods according to present principlesprovide dynamic reports based on available data and devices. Forexample, useless sections of the report, such as those with no populateddata, may be removed, minimized in importance, assigned a lowerpriority, or the like.

In general, insights gleaned may be prioritized. Reports may be builtthat remove sections that are not supported by available data. Reportsmay also be built in which sections are eliminated or prioritized tohighlight information that is believed to be of most relevance to theuser of the report. In this way, dynamic reports may be built,constructed, generated, or created based on what is determined to be ofimportance to the user. That is, systems and methods according topresent principles may identify what information would likely be ofimportance to the user, e.g., based on an analysis of the availabledata, and may present the findings dynamically, such that importantfindings are presented and prioritized in the report.

In another aspect, the design of the report may accommodate a variety ofuser types and use cases. In this way, reports may be provided which areuseful for a broad range of users. In one particular implementation, arelatively simple initial view is provided, but the user may be allowedand enabled to “drill down” into details if so desired.

In yet another aspect, reports may be dynamically generated so as toguide a doctor-patient conversation. In this way, the report may beorganized so that the same guides the conversation in a logical, flowingmanner, reducing the amount of data presented that is unimportant orless important to the discussion.

In yet another aspect, reports may be dynamically generated in such away so as to effectively coach the patient. In this manner, the reportnot only points out problem areas, but also celebrates progress. Thereport can specifically point out where the patient has done well, suchas “good days”. In the same way, the report need not highlight everyproblem, but rather identify the most problematic and focus on the same.In a particular implementation, the dynamically generated reports canfocus on one particular problem at a time, so as to make user behaviormodification more gradual and more convenient. The particular problemfocused on may be that which is most problematic.

In yet another aspect, reports may be dynamically generated in such away as to be more intuitive, not necessarily following old conventionsthat are less so. Information may be displayed in a way that reflectshow the information affects the user/patient. For example, in aglucose/diabetes management implementation, in one implementation,received insulin may be displayed so as to “push down” on the patient'sglucose levels, while ingested carbohydrates may be displayed in a wayso as to “push up” the patient's glucose levels.

In another implementation, useful information may be generated anddisplayed about how long particular events are or have been affectingthe user's glucose levels. Such “duration” data may be particularlyuseful in the analysis and prognosis of long-term effects ofhypoglycemia or hyperglycemia.

In a first aspect, a method is provided of dynamically reporting dataabout a user, including: receiving a set of available data about a user,the set corresponding to a first set of available data fields; receivinga default data presentation template, the received default datapresentation template having a second set of available data fields anddata visualizations based on the second set of available data fields;modifying the default data presentation template, the modifyingincluding: removing data fields from the second set that are not in thefirst set or are not determinable from the first set, removing datavisualizations not determinable from the first set, populating themodified default data presentation template, including the data fieldsand the data visualizations, with the received set of available data,and displaying the populated modified default data presentationtemplate.

Implementations may include one or more of the following. The receivinga default data presentation template may be preceded by receiving aselection from a user of a default data presentation template. Thereceiving a selection from a user may be preceded by displaying a set ofavailable default data presentation templates. The data may correspondat least in part to an analyte concentration such as a glucoseconcentration. The second set may include data fields and datavisualizations covering a default time frame, and the modifying mayinclude reducing the second set to only include data fields and datavisualizations covering a time frame to which the received availabledata corresponds. The modifying may include prioritizing the fields andvisualizations in the modified default data presentation template, suchthat upon the displaying, fields and visualizations with a higherpriority are displayed above those with a lower priority. Theprioritizing may be such that CGM data is given a higher priority thanSMBG data. The modifying may include displaying CGM fields if available,and if not, displaying SMBG fields. The default data presentationtemplate may include a data field or visualization corresponding toinsulin. The default data presentation template may include a data fieldor visualization corresponding to events.

The modifying may further include: identifying a pattern in the receiveddata; and modifying the default data presentation template to include adata visualization corresponding to the identified pattern. Theidentified pattern may include a series of measured glucose values withrespect to time. The identifying may include: quantifying a similarityin the received data over two or more periods of time; if the quantifiedsimilarity is greater than a predetermined threshold criterion, thenidentifying the similarity as a pattern. The method may further includeprioritizing the data visualizations corresponding to the identifiedpatterns, and may further include displaying the data visualizationscorresponding to higher priority patterns above data visualizationscorresponding to lower priority patterns. The identified pattern may beselected from the group consisting of: overnight lows, post-meal highs,post-meal lows, time of day highs, time of day lows, weekend versusweekday highs/lows, post event highs/lows, and best days. The method mayfurther include identifying at least one event preceding a pattern, andmay further include modifying the default data presentation template toinclude a data field or data visualization corresponding to theidentified at least one event.

The data visualization corresponding to the identified pattern may be achart, and the data field or data visualization corresponding to theidentified at least one event may be an icon placed on the chart. Theidentifying at least one event may include comparing data about eventsto predetermined event criteria. The data field or data visualizationcorresponding to the identified at least one event may include dataabout a magnitude of the event, an average of similar events, or anamount of time for which the identified event preceded the identifiedpattern. The method may further include receiving a user entrycorresponding to the event, and storing the user entry along with dataabout the identified event.

The modifying may further include displaying a suggestion based on thereceived available data, and the suggestion may be further based on apattern identified in the received data.

The modifying may further include modifying the default datapresentation template to include a data visualization corresponding toat least one signal trace of a measured glucose value with respect totime, and may further include displaying an indicator of insulin intakeand/or carbohydrate ingestion, and the indicator of insulin intake maybe displayed above the at least one signal trace whereby the indicatorof insulin intake may be read as “pushing down” on the at least onesignal trace, and the indicator of carbohydrate ingestion may bedisplayed below the at least one signal trace whereby the indicator ofcarbohydrate ingestion may be read as “pushing up” on the at least onesignal trace.

The at least one signal trace of a measured glucose value with respectto time may include a plurality of signal traces corresponding to themeasured glucose values with respect to a like time period. Theindicator of insulin intake may be quantified and quantized, e.g., basalinsulin may be indicated by a constant level on the trace graph and oneor more boluses of insulin may be indicated by one or more respectiveicons at a position with respect to time on the trace graph at which theone or more boluses were caused by the user. If a cessation or reductionin the basal insulin occurs, the basal insulin indication on the tracegraph may be correspondingly modified. The method may further includeshaping the one or more boluses of insulin to have an extended tail,whereby a length and magnitude of an effect of the bolus is conveyed toa viewer. Similarly, the indicator of carbohydrate ingestion may bequantified and quantized, such that one or more units of carbohydratesare indicated by one or more respective icons at a position with respectto time on the trace graph at which the one or more units ofcarbohydrates were ingested by the user.

The modifying may further include modifying the default datapresentation template to include a data visualization corresponding toat least one signal trace of a measured glucose value with respect totime, the at least one signal trace having a first color, the at leastone signal trace being displayed in a second color for values of thesignal trace above a predetermined threshold, the at least one signaltrace being displayed in a third color for values of the signal tracebelow another predetermined threshold. The at least one signal trace mayinclude a plurality of signal traces, and the plurality of signal tracesmay be displayed as part of the data visualization using variabilitybars. The data visualization may further include an indication of analarm, the alarm associated with an alarm symbol and an alarm value. Thepredetermined threshold may correspond to a hyperglycemic level orurgency and the another predetermined threshold may correspond to ahypoglycemic level or urgency. The method may further include causingthe predetermined threshold, or the another predetermined threshold, orboth, to vary as a function of time of day or patient activity. Thepatient activity may correspond to eating, bolusing insulin, exercising,or a combination of the above. The method may further include indicatinga variation of the predetermined threshold or the another predeterminedthreshold on the data visualization. The method may further includecolor coding, or indicating by distinct symbols, the predeterminedthreshold, or the another predetermined threshold, or both, and/or thevariation of the predetermined threshold or the another predeterminedthreshold, on the data visualization.

The method may further include receiving an entry corresponding to thepredetermined threshold, the another predetermined threshold, or both.The entry may be received from a computing environment associated with ahealth care professional, whereby the health care professional can setthresholds for a plurality of users. The method may further includeautomatically setting the predetermined threshold and the anotherpredetermined threshold based on one or more factors selected from thegroup consisting of: age, insurance, type I versus type II, or a glucosecontrol metric. At least a portion of the received available data maycorrespond to a blood glucose measurement, at least another portion ofthe received available data corresponds to blood glucose calibrationdata, and the modifying the default data presentation template toinclude data visualization may include displaying blood glucosemeasurement data differently from blood glucose calibration data.

The modifying may include modifying the default data presentationtemplate to include a data visualization, and by hovering over a portionof the data visualization, additional information about the portion maybe displayed. The modifying may include modifying the default datapresentation template to include a data visualization, and delete byselecting a portion of the data visualization, additional informationabout the portion may be displayed. The modifying may include modifyingthe default data presentation template to include a data visualization,and by varying a timeframe, the data visualization may be automaticallyupdated to reflect received data pertaining to the varied timeframe. Theportion may correspond to a pattern, and the selection may result in adata visualization being displayed including one or more featuresselected from the group consisting of: an overview, a multi-day chartsillustrating the pattern, a plurality of single day charts illustratingthe pattern, and identified event preceding the pattern, and/or asuggestion related to the pattern.

The method may further include receiving an indication of a desired timeframe. The indication may be received from user selection of one or morecalendar dates. The indication may be received from user selection of anevent. The desired time frame may be a first duration of time before theevent and a second duration of time after the event. The modifying mayinclude modifying the default data presentation template to include anaction item list including a list of entries of action items. Themodifying may include modifying the default data presentation templateto include a device usage list, the list including a list of entries ofdevices, and upon selection of an entry from the list additional detailmay be displayed about usage of the device.

The modifying may further include modifying the default datapresentation template to include a data visualization including compareddata, where the compared data compares equivalent data visualizationsfrom two different like time periods. The compared data may include oneor more selected from the group consisting of: a chart of a signal traceof a measured glucose value with respect to time, an indicator of deviceusage, an indicator of an identified pattern, or statistics about themeasured data.

The modifying may further include modifying the default datapresentation template to include a data visualization includingperformance data, where a health care professional may view performanceof one or more selected patients according to selected criteria, such ascriteria selected from the group consisting of: age, weight, sex,insurance, length of time as a patient, type I versus type II, devicesused, events, or therapy regimes. The method may further includegrouping patients by individual, clinician, or group. The method mayfurther include monitoring patient compliance per group. The method mayfurther include monitoring patient performance per group by comparingpatient performance against performance criteria, where the criteriainclude one or more selected from the group consisting of: A1C, detectedpatterns, compliance with therapy, and the data visualization mayinclude a comparison of patients that comply with a particular therapy.The displaying may further include printing.

In a second aspect, a non-transitory computer readable medium isprovided, including instructions for causing a computing environment toperform the above method.

In third aspect, a method is provided of dynamically reporting dataabout a user, including: receiving a set of available data about a user,the set corresponding to a first set of available data fields; based onthe received set of available data, creating a data presentationtemplate, the created data presentation template having a second set ofavailable data fields or data visualizations corresponding to orgenerated from the first set; populating the created data presentationtemplate with the received set of available data; and displaying thepopulated created data presentation template.

Implementations may include one or more of the following. The creating adata presentation template may be preceded by receiving a selection froma user of a desired data presentation template. The receiving aselection from a user may be preceded by displaying a set of availabledata presentation templates. The data may correspond at least in part toan analyte concentration such as glucose. The second set may includedata fields and data visualizations covering a time frame, and thecreating may include matching the second set to include data fields anddata visualizations covering a time frame to which the receivedavailable data corresponds. The creating may include prioritizing thefields and visualizations in the data presentation template, such thatupon the displaying, fields and visualizations with a higher priorityare displayed above those with a lower priority. The prioritizing may besuch that CGM data is given a higher priority than SMBG data. Thecreating may include displaying CGM fields if available, and if not,displaying SMBG fields. The created data presentation template mayinclude a data field or visualization corresponding to insulin. Thecreated data presentation template may include a data field orvisualization corresponding to events.

The creating may include: identifying a pattern in the received data;and creating the data presentation template to include a datavisualization corresponding to the identified pattern. The identifiedpattern may include a series of measured glucose values with respect totime. The identifying may include: quantifying a similarity in thereceived data over two or more like periods of time; if the quantifiedsimilarity is greater than a predetermined threshold criterion, thenidentifying the similarity as a pattern.

The method may further include prioritizing the data visualizationscorresponding to the identified patterns, and may further includedisplaying the data visualizations corresponding to higher prioritypatterns above data visualizations corresponding to lower prioritypatterns. The identified pattern may be selected from the groupconsisting of: overnight lows, post-meal highs, post-meal lows, time ofday highs, time of day lows, weekend versus weekday highs/lows, postevent highs/lows, and best days. The method may further includeidentifying at least one event preceding a pattern, and creating thedata presentation template to include a data field or data visualizationcorresponding to the identified at least one event.

The data visualization corresponding to the identified pattern may be achart, and the data field or data visualization corresponding to theidentified at least one event may be an icon placed on the chart. Theidentifying at least one event may include comparing data about eventsto predetermined event criteria. The data field or data visualizationcorresponding to the identified at least one event may include dataabout a magnitude of the event, an average of similar events, or anamount of time for which the identified event preceded the identifiedpattern. The method may further include receiving a user entrycorresponding to the event, and storing the user entry along with dataabout the identified event. The creating may include displaying asuggestion based on the received available data. The suggestion may befurther based on a pattern identified in the received data.

The creating may include creating the data presentation template toinclude a data visualization corresponding to at least one signal traceof a measured glucose value with respect to time, and may furtherinclude displaying an indicator of insulin intake and/or carbohydrateingestion, where the indicator of insulin intake is displayed above theat least one signal trace whereby the indicator of insulin intake may beread as “pushing down” on the at least one signal trace, and where theindicator of carbohydrate ingestion is displayed below the at least onesignal trace whereby the indicator of carbohydrate ingestion may be readas “pushing up” on the at least one signal trace.

The at least one signal trace of a measured glucose value with respectto time may include a plurality of signal traces corresponding to themeasured glucose values with respect to a like time period. Theindicator of insulin intake may be quantified and quantized, such thatbasal insulin is indicated by a constant level on the trace graph andone or more boluses of insulin are indicated by one or more respectiveicons at a position with respect to time on the trace graph at which theone or more boluses were caused by the user. If a cessation or reductionin the basal insulin occurs, the basal insulin indication on the tracegraph may be correspondingly modified. The method may further includeshaping the one or more boluses of insulin to have an extended tail,whereby a length and magnitude of an effect of the bolus may be conveyedto a viewer. The indicator of carbohydrate ingestion may be quantifiedand quantized, such that one or more units of carbohydrates may beindicated by one or more respective icons at a position with respect totime on the trace graph at which the one or more units of carbohydrateswere ingested by the user.

The creating may include creating the data presentation template toinclude a data visualization corresponding to at least one signal traceof a measured glucose value with respect to time, the at least onesignal trace having a first color, the at least one signal trace beingdisplayed in a second color for values of the signal trace above apredetermined threshold, the at least one signal trace being displayedin a third color for values of the signal trace below anotherpredetermined threshold. The at least one signal trace may include aplurality of signal traces, and the plurality of signal traces may bedisplayed as part of the data visualization using variability bars. Thedata visualization may further include an indication of an alarm, thealarm associated with an alarm symbol and an alarm value. Thepredetermined threshold may correspond to a hyperglycemic level orurgency and the another predetermined threshold may correspond to ahypoglycemic level or urgency. The method may further include causingthe predetermined threshold, or the another predetermined threshold, orboth, to vary as a function of time of day or patient activity. Thepatient activity may correspond to eating, bolusing insulin, exercising,or a combination of the above. The method may further include indicatinga variation of the predetermined threshold or the another predeterminedthreshold on the data visualization. The method may further includecolor coding, or indicating by distinct symbols, the predeterminedthreshold, or the another predetermined threshold, or both, and/or thevariation of the predetermined threshold or the another predeterminedthreshold, on the data visualization.

The method may further include receiving an entry corresponding to thepredetermined threshold, the another predetermined threshold, or both.The entry may be received from a computing environment associated with ahealth care professional, whereby the health care professional can setthresholds for a plurality of users. The method may further includeautomatically setting the predetermined threshold and the anotherpredetermined threshold based on one or more factors selected from thegroup consisting of: age, insurance, type I versus type II, or a glucosecontrol metric. At least a portion of the received available data maycorrespond to blood glucose measurements, at least another portion ofthe received available data may correspond to blood glucose calibrationdata, and the creating the data presentation template to include a datavisualization may include displaying blood glucose measurement datadifferently from blood glucose calibration data.

The creating may include creating the data presentation template toinclude a data visualization, and by hovering over a portion of the datavisualization, additional information about the portion may bedisplayed. The creating may include creating the data presentationtemplate to include a data visualization, and by selecting a portion ofthe data visualization, additional information about the portion may bedisplayed. The creating may include creating the data presentationtemplate to include a data visualization, and by varying a timeframe,the data visualization may be automatically updated to reflect receiveddata pertaining to the varied timeframe. The portion may correspond to apattern, and the selection may result in a data visualization beingdisplayed including one or more features selected from the groupconsisting of: an overview, a multi-day chart illustrating the pattern,a plurality of single day charts illustrating the pattern, an identifiedevent preceding the pattern, and/or a suggestion related to the pattern.The method may further include receiving an indication of a desired timeframe. The indication may be received from a user selection of one ormore calendar dates. The indication may be received from a userselection of an event. The desired time frame may be a first duration oftime before the event and a second duration of time after the event. Thecreating may include creating the data presentation template to includean action item list including a list of entries of action items. Thecreating may include creating the data presentation template to includea device usage list, the list including a list of entries of devices,and upon selection of an entry from the list, additional detail may bedisplayed about usage of the device.

The creating may include creating the data presentation template toinclude a data visualization including compared data, where the compareddata compares equivalent data visualizations from two different liketime periods. The compared data may include one or more selected fromthe group consisting of: a chart of a signal trace of a measured glucosevalue with respect to time, an indicator of device usage, an indicatorof an identified pattern, or statistics about the measured data.

The creating may include creating the data presentation template toinclude a data visualization including performance data, where a healthcare professional may view performance of one or more selected patientsaccording to selected criteria, where the criteria is selected from thegroup consisting of: age, weight, sex, insurance, length of time as apatient, type I versus type II, devices used, events, or therapyregimes. The method may further include grouping patients by individual,clinician, or group. The method may further include monitoring patientcompliance per group. The method may further include monitoring patientperformance per group by comparing patient performance againstperformance criteria, where the criteria include one or more selectedfrom the group consisting of: A1C, detected patterns, compliance withtherapy. The criteria may also include compliance with therapy, and thedata visualization may include a comparison of patients that comply witha particular therapy.

In a fourth aspect, a non-transitory computer readable medium isprovided, including instructions for causing a computing environment toperform the above method.

In a fifth aspect, method is provided of dynamically reporting dataabout a user, including: receiving a set of available data about a user,the set corresponding to a first set of available data fields; receivinga default data presentation template, the received default datapresentation template having a second set of available data fields ordata visualizations based on the second set of available data fields;populating the default data presentation template, including the datafields and the data visualizations, with the received set of availabledata; prioritizing the data fields and the data visualizations in thedefault data presentation template; and displaying the populated defaultdata presentation template.

Implementations may include one or more of the following. The method mayfurther include modifying the default data presentation template, themodifying including, prior to the prioritizing or displaying: removingdata fields from the second set that are not in the first set or are notdeterminable from the first set and removing data visualizations notdeterminable from the first set. The prioritizing may include orderingthe displayed data fields and data visualizations such that higherpriority fields and visualizations are displayed before lower priorityfields and visualizations. The prioritizing may include highlighting thedisplayed data fields and data visualizations such that higher priorityfields and visualizations are highlighted differently than lowerpriority fields and visualizations. The prioritizing may includecomparing the populated data fields and data visualizations against aset of criteria. The set of criteria may be entered by a user or may beset by default.

In a sixth aspect, a method is provided of dynamically reporting dataabout a user, including: receiving a set of available data about a user,the set corresponding to a first set of available data fields; based onthe received set of available data, creating a data presentationtemplate, the created data presentation template having a second set ofavailable data fields and data visualizations corresponding to the firstset; populating the created data presentation template with the receivedset of available data; prioritizing the populated data fields and datavisualizations; and displaying the populated created data presentationtemplate.

Implementations may include one or more of the following. The method mayfurther include modifying the default data presentation template, themodifying including, prior to the prioritizing or displaying: removingdata fields from the second set that are not in the first set or are notdeterminable from the first set and removing data visualizations notdeterminable from the first set. The prioritizing may include orderingthe displayed data fields and data visualizations such that higherpriority fields and visualizations are displayed before lower priorityfields and visualizations. The prioritizing may further includehighlighting the displayed data fields and data visualizations such thathigher priority fields and visualizations are highlighted differentlythan lower priority fields and visualizations. The prioritizing mayfurther include comparing the populated data fields and datavisualizations against a set of criteria. The set of criteria may beentered by a user or may be set by default.

In a seventh aspect, a system is provided for performing any of themethods described below. In yet another aspect, a device, system, and/ormethod substantially as shown and/or described in the specificationand/or drawings are provided.

In an eighth aspect, an electronic device is provided for monitoringdata associated with a physiological condition, including: a continuousanalyte sensor, where the continuous analyte sensor is configured tosubstantially continuously measure the concentration of analyte in thehost, and to provide continuous sensor data associated with the analyteconcentration in the host; and a processor module configured to performany one of the methods described here.

In a ninth aspect, an electronic device is provided for delivering amedicament to a host, the device including: a medicament delivery deviceconfigured to deliver medicament to the host, where the medicamentdelivery device is operably connected to a continuous analyte sensor,where the continuous analyte sensor is configured to substantiallycontinuously measure the concentration of analyte in the host, and toprovide continuous sensor data associated with the analyte concentrationin the host; and a processor module configured to perform any one of themethods described here.

In a tenth aspect, method is provided of identifying and reportingevents preceding a pattern in a set of user data, including: receiving aset of data about a user; identifying a pattern in the received data,the pattern representing repeating data arrangements in the receiveddata; displaying a data visualization corresponding to the identifiedpattern; identifying at least one event preceding one or more of therepeating data arrangements; and displaying an indication of theidentified event on the displayed data visualization.

Implementations may include one or more of the following. Theidentifying at least one event may further include identifying at leastone event preceding at least a predetermined percentage of the repeatingdata arrangements. The percentage may be at least 25%, 50%, 75%, 90%, or99%. The method may further include displaying an icon corresponding tothe event on the displayed data visualization. The method may furtherinclude displaying data in a window, frame, or layer, corresponding tothe event, on or within the displayed data visualization. The datacorresponding to the event may include data about a nature of the event,an average amount of time between the event and a start of the pattern,or an effect of the event, or combinations of the above. The method mayfurther include displaying an editable field along with the indicationof the identified event, whereby a user can enter and store informationabout the event. The identifying at least one event may further includecomparing an event against one or more criteria to determine if theevent pertains to the identified pattern. The data may be a glucoseconcentration value, and the identifying at least one event may furtherinclude identifying at least one increase or decrease in glucose valuepreceding two or more of the repeating data arrangements.

In an eleventh aspect, a method is provided of displaying datapertaining to user intake of a substance, e.g., one or more units of asubstance, including: receiving a first set of data about a user, thefirst set of data representing an analyte concentration value withrespect to time; receiving a second set of data about a user, the secondset of data representing user intake of a substance with respect totime; aligning the first and second sets of data with respect to timeand displaying the first and second sets of data in one datavisualization, where the second set of data is displayed such that anindicator of substances that tend to increase the analyte value isdisplayed below the first set of data, and an indicator of substancesthat tend to decrease the analyte value are displayed above the firstset of data.

Implementations may include one or more of the following. The second setof data may further represent user intake of one or more units of thesubstance with respect to time and the second set of data may bedisplayed such that substances that tend to increase the analyte valueare displayed as individual units with a quantity according to the userintake and below the first set of data, and substances that tend todecrease the analyte value may be displayed as individual units with aquantity according to the user intake and above the first set of data.The first set of data may represent a signal trace of glucoseconcentration and the second set of data may represent insulin intakeand/or carbohydrate ingestion, where units of insulin intake may bedisplayed above the signal trace, whereby the units of insulin intakeappear to “push down” on the signal trace, and where units ofcarbohydrate ingestion may be displayed below the signal trace, wherebythe units of carbohydrate ingestion appear to “push up” on the signaltrace. The method may further include: receiving a basal value of amedicament, and displaying an indicator of the basal value on the datavisualization.

In a twelfth aspect, a method is provided of displaying data pertainingto user analyte concentration values, including: receiving a set of dataabout a user, the first set of data representing an analyteconcentration value with respect to time; receiving at least onethreshold value; and displaying the set of data such that the abscissais time and the ordinate is the analyte concentration value, where thedisplaying is such that values of the analyte concentration value abovethe threshold are displayed using a different format than values of theanalyte concentration value below the threshold.

Implementations may include one or more of the following. The method mayfurther include displaying an indicator of the received threshold valuealong with the displayed set of data. The method may further include:receiving at least one alert value and displaying an indication of theat least one alert value along with the displayed set of data. Themethod may further include: receiving at least one alarm value anddisplaying an indication of the at least one alarm value along with thedisplayed set of data. The threshold value may vary with respect totime, including periodically over time, such as where the period is oneday. The threshold value may also be entered by a user. The analyte maybe glucose, the threshold value may be a default value, and the defaultvalue may be determined based on at least one of: user age, userinsurance, user type of diabetes, user glucose control metric, A1Cvalue, user mean glucose value, or user mean glucose variability.

In a thirteenth aspect, a method is provided of displaying datapertaining to user analyte concentration values, including: receiving afirst set of data about a user, the first set of data representinganalyte concentration values with respect to time measured using a firsttechnique; receiving a second set of data about the user, the second setof data representing calibration values of the analyte concentrationvalue with respect to time measured using a second technique, where thefirst set of data is calibrated based on the second set of data;receiving a third set of data about the user, the third set of datarepresenting values of the analyte concentration value with respect totime measured using the second technique; and displaying the first,second, and third sets of data such that the abscissa is time and afirst set of ordinates is the first set of data and another set ofordinates is the third set of data.

Implementations may include one or more of the following. The method mayfurther include displaying a third set of ordinates, the third set ofordinates corresponding to the second set of data, where the ordinatesrepresenting the second set of data and the ordinates representing thethird set of data are formatted differently. The first set of data maybe glucose measurements from a continuous glucose monitor, the secondset of data may be calibration measurements from a blood glucosemonitor, and the third set of data may be measurements from a bloodglucose monitor.

In a fourteenth aspect, a method is provided of displaying datapertaining to user analyte concentration values, including: receiving afirst set of data about a user, the first set of data representing ananalyte concentration value with respect to time over a first timeperiod; receiving a second set of data about a user, the second set ofdata representing an analyte concentration value with respect to timeover a second time period; and displaying the first set of data as adata visualization and displaying the second set of data as a datavisualization, the data visualizations adjacent each other, whereby auser may compare the analyte concentration value over the first timeperiod with the analyte concentration value over the second time period.

Implementations may include one or more of the following. The method mayfurther include identifying a pattern in either the first or second setof received data, or both, the pattern representing repeating dataarrangements in the received data, and displaying an indication of thepattern along with the respective data visualization. If the first timeperiod is prior to the second time period, and if a pattern isidentified in the first set of data but not the second set of data, thenthe method may further include displaying an indication that the patternis not present in the second set of data. The first and second timeperiods may be selected by default. The method may further includedetecting an event in a user calendar, and the first and second timeperiods may be selected as being before and after the event. The eventmay be an appointment with a healthcare practitioner. The first andsecond time periods may be configured to be selectable by a user. Themethod may further include identifying device usage associated witheither the first or second set of received data, or both, and displayingan indication of the identified device usage along with the respectivedata visualizations. The method may further include identifying a usermetric associated with either the first or second set of received data,or both, and displaying an indication of the identified user metricalong with the respective data visualizations. The first and second timeperiods may be determined based on an identified pattern.

In a fifteenth aspect, a method is provided of displaying data aboutpatients in a multi-patient setting, including: receiving in a userinterface one or more criteria related to patient data; comparing theone or more received criteria against data records in a databaseincluding a set of patient records; and determining and displaying inthe user interface one or more patient records that meet the receivedcriteria.

Implementations may include that the one or more criteria are selectedfrom the group consisting of: age, weight, gender, insurance, length oftime as a patient, type of malady, devices used to monitor or treat amalady, events associated with patient, a therapy regime, criteriarelated to user malady treatment performance, or combinations of theabove.

In a sixteenth aspect, a method is provided of displaying data aboutpatient compliance in a multi-patient setting, including: receiving in auser interface one or more criteria related to patient compliance with atherapy regime; comparing the one or more criteria against data recordsin a database including a set of patient records; and determining anddisplaying in the user interface one or more patient records that meetthe received criteria.

Implementations may include that the one or more criteria are selectedfrom the group consisting of: accuracy of device usage, overall time ofdevice usage, accuracy of calibration measurements with respect to asuggested calibration time, user acknowledgement of alarms, accuracy ofmedicament administration, or combinations of the above.

Advantages may include, in certain embodiments, one or more of thefollowing. Dynamic reports provided by systems and methods according topresent principles may coach the patient in a way tailored to thepatient or otherwise particularly convenient to the same. The dynamicreports can be dynamically formatted and edited to guide adoctor-patient conversation. Such dynamic reports may not necessarilysimply point out problem areas, e.g., highlighting every problem, butcan provide a useful tool for patients/users and health careproviders/practitioners (“HCP”s) and caregivers to guide diseasemanagement. Other advantages will be understood from the descriptionthat follows, including the figures.

Any of the features of embodiments of the various aspects disclosed isapplicable to all aspects and embodiments identified. Moreover, any ofthe features of an embodiment is independently combinable, partly orwholly with other embodiments described herein, in any way, e.g., one,two, or three or more embodiments may be combinable in whole or in part.Further, any of the features of an embodiment of the various aspects maybe made optional to other aspects or embodiments. Any aspect orembodiment of a method can be performed by a system or apparatus ofanother aspect or embodiment, and any aspect or embodiment of the systemcan be configured to perform a method of another aspect or embodiment.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

It is to be understood that both the foregoing general description andthe following detailed description are example and explanatory only andare not restrictive. Further features and/or variations may be providedin addition to those set forth herein. For example, the implementationsdescribed herein may be directed to various combinations andsubcombinations of the disclosed features and/or combinations andsubcombinations of several further features disclosed below in thedetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a high-level system architecture of a remote monitoringsystem in accordance with some exemplary implementations;

FIG. 2A illustrates a page flow of an exemplary dynamic reporting systemaccording to present principles;

FIG. 2B is a layout of a user interface of a dynamic exemplary reportingsystem according to present principles;

FIG. 2C is a flowchart of a first exemplary method of creating dynamicreports according to present principles;

FIG. 3 is a more detailed layout of a portion of an exemplary reportingsystem according to present principles, which may be employed within thecontext of either a patient or an HCP view;

FIG. 4 is a more detailed layout of a portion of an exemplary dynamicreporting system according to present principles, which may be employedwithin the context of an HCP view;

FIG. 5 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a CGM overview page including asummary as well as windows pertaining to patterns, action items, anddevice usage;

FIG. 6 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a BG summary as well as windowspertaining to patterns, action items, and device usage (according todynamic reporting principles herein, in some cases fewer data fields andvisualizations will be shown as compared to reports incorporating CGMdata);

FIG. 7 is a more detailed exemplary user interface of a dynamicreporting system according to present principles, showing a CGM summary;

FIG. 8 is a more detailed exemplary user interface of a dynamicreporting system according to present principles, showing a BG summary;

FIG. 9 is a flowchart of a second exemplary method of a dynamicreporting system according to present principles;

FIG. 10 is a flowchart of a third exemplary method of a dynamicreporting system according to present principles;

FIG. 11 is a flowchart of a fourth exemplary method of a dynamicreporting system according to present principles;

FIG. 12 is a flowchart of a fifth exemplary method of a dynamicreporting system according to present principles;

FIG. 13 is a flowchart of a sixth exemplary method of a dynamicreporting system according to present principles;

FIG. 14 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a set of patterns determinedfrom CGM data;

FIG. 15 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a set of patterns determinedfrom BG data;

FIG. 16 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a set of proposed actionsdetermined from received monitoring data;

FIG. 17 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a history of action items;

FIG. 18 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a displayed set of devicesassociated with the patient or user being monitored;

FIG. 19 is a more detailed exemplary user interface of a dynamicreporting system according to present principles, showing a displayedset of devices associated with the patient or user being monitored, withan expanded section providing additional detail about a pump beingportrayed;

FIG. 20 is an exemplary user interface of a dynamic reporting systemaccording to present principles, applied to the case of glucosemonitoring, and showing a displayed set of A1C data;

FIG. 21 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing a view of displayed data,including a window for patterns, events preceding patterns, questionsfor a provider, and suggestions/tips;

FIG. 22 is a more detailed pattern graph employable with a dynamicreporting system, showing variability bars, as well as indications forinsulin/carbohydrate ingestion;

FIG. 23 is another more detailed pattern graph employable with a dynamicreporting system, showing variability bars, as well as indications forinsulin/carbohydrate ingestion;

FIG. 24 is another more detailed graph employable with a dynamicreporting system, showing a trace for a single day, and illustratingevents associated with a pattern;

FIG. 25 is an exemplary user interface of a dynamic reporting systemaccording to present principles, showing data patterns for BG data overa weekend period of time;

FIG. 26 is an exemplary user interface of a dynamic reporting systemaccording to present principles, including expandable windows forpatterns and device usage;

FIG. 27A shows an exemplary user interface of a dynamic reporting systemillustrating a comparison of patterns over different periods of time;

FIG. 27B shows another exemplary user interface of a dynamic reportingsystem illustrating a comparison of more detailed patterns overdifferent periods of time;

FIG. 27C shows another exemplary user interface of a dynamic reportingsystem illustrating a comparison of patterns over different periods oftime;

FIG. 28 shows an exemplary user interface of a dynamic reporting system,and in particular a profile edit screen;

FIG. 29 shows an exemplary user interface of a dynamic reporting system,and in particular for use by an HCP, for monitoring a group of patients;

FIG. 30 shows another exemplary user interface of a dynamic reportingsystem, for use by an HCP, for monitoring a group of patients;

FIG. 31 shows another exemplary user interface of a dynamic reportingsystem, for use by an HCP, for monitoring a group of patients, showingfilter options;

FIG. 32 shows another exemplary user interface of a dynamic reportingsystem, for use by an HCP, for monitoring and editing information aboutclinicians within a clinic;

FIGS. 33 and 34 illustrate the setting of print options for dynamicreports according to present principles;

FIG. 35 illustrates an exemplary printed dynamically generated orcreated report of CGM and other data according to present principles;

FIG. 36 illustrates an exemplary printed dynamically generated orcreated report of BG and other data according to present principles;

FIG. 37 shows an exemplary printed dynamic report, illustrating aparticular pattern within the monitored data, in a set of single dayviews;

FIG. 38 shows an exemplary printed dynamic report, in particularillustrating devices and usage;

FIG. 39 shows an exemplary printed dynamic report, in particularillustrating patterns within the monitored data;

FIG. 40 shows a similar printed dynamic report as FIG. 39, but thischart including calibration points;

FIG. 41 shows an exemplary printed dynamic report, illustrating aparticular pattern within the monitored data;

FIG. 42 shows an exemplary printed dynamic report, in particularillustrating a view of a week's worth of monitored CGM trace data;

FIG. 43 shows an exemplary printed dynamic report, in particularillustrating a view of a week's worth of monitored BG trace data;

FIG. 44 shows an exemplary printed dynamic report, in particularillustrating BG data;

FIG. 45 illustrates an exemplary hovering process to obtain additionaldata within a dynamically generated or created report;

FIG. 46 illustrates another exemplary hovering process to obtainadditional data within a dynamically generated or created report;

FIG. 47 is a schematic view of a receiver or host (or caregiver/patient)monitor, in the form of a smart phone; and

FIG. 48 is a block diagram of receiver electronics in one embodiment.

Like reference numerals refer to like elements throughout. Elements arenot to scale unless otherwise noted.

DETAILED DESCRIPTION Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is glucose. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free B-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The terms “microprocessor” and “processor” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto a computer system, state machine, and the like that performsarithmetic and logic operations using logic circuitry that responds toand processes the basic instructions that drive a computer.

The term “RF transceiver” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The term “calibration” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the process of determining therelationship between the sensor data and the corresponding referencedata, which can be used to convert sensor data into meaningful valuessubstantially equivalent to the reference data. In some embodiments,namely, in continuous analyte sensors, calibration can be updated orrecalibrated over time as changes in the relationship between the sensordata and reference data occur, for example, due to changes insensitivity, baseline, transport, metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, to provide ameaningful value to a user.

The term “algorithm” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process (forexample, programs) involved in transforming information from one stateto another, for example, by using computer processing.

The term “sensor” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component or region of adevice by which an analyte can be quantified.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample” as used herein are broad terms and areto be given their ordinary and customary meaning to a person of ordinaryskill in the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to any mechanism(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction:

Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto one or more components being linked to another component(s) in amanner that allows transmission of signals between the components. Forexample, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal, e.g., anelectrical or electromagnetic signal; the signal can then be transmittedto an electronic circuit. In this case, the electrode is “operablylinked” to the electronic circuitry. These terms are broad enough toinclude wireless connectivity.

The term “determining” encompasses a wide variety of actions. Forexample, “determining” may include calculating, computing, processing,deriving, investigating, looking up (e.g., looking up in a table, adatabase or another data structure), ascertaining and the like. Also,“determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, calculating,deriving, establishing and/or the like.

The term “electronic circuitry” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the components ofa device configured to process biological information obtained from ahost. In the case of a glucose-measuring device, the biologicalinformation is obtained by a sensor regarding a particular glucose in abiological fluid, thereby providing data regarding the amount of thatglucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398,which are hereby incorporated by reference, describe suitable electroniccircuits that can be utilized with devices including the biointerfacemembrane of a preferred embodiment.

The term “substantially” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “host” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammals, particularly humans.

The term “continuous analyte (or glucose) sensor” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to a device that continuously or continually measures aconcentration of an analyte, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the continuous analyte sensor is a glucosesensor such as described in U.S. Pat. No. 6,001,067, which isincorporated herein by reference in its entirety.

The term “continuous analyte (or glucose) sensing” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to the period in which monitoring of an analyte iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein are broad terms and are to begiven their ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to a device thatmeasures a concentration of an analyte and can be used as a referencefor the continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, and the like.

The term “mean” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the sum of the observationsdivided by the number of observations.

The term “variation” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a divergence or amount ofchange from a point, line, or set of data. In one embodiment, estimatedanalyte values can have a variation including a range of values outsideof the estimated analyte values that represent a range of possibilitiesbased on known physiological patterns, for example.

The term “measured analyte values” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includedata from the analyte sensor before or after data processing in thesensor and/or receiver (for example, data smoothing, calibration, andthe like).

The term “estimated analyte values” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

The following description and examples described the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

Implementations described herein can include a system and method fordynamic generation of reports about health characteristics of one ormore hosts. The health characteristics can include an analyteconcentration of a host, such as glucose, or a bodily function, such asheart rate, blood pressure, temperature and the like. In addition, othercharacteristics of a host can be monitored and reported about tofacilitate care of a host, such as a location of the host, state of ahost (e.g., exercising, sleeping, or working), medication ingested bythe host, meals consumed by the host and the like. The dynamicallygenerated reports can include information about patterns detected in themonitored analyte concentration, steps to take upon detection of apattern, reasons for patterns, noted events, reasons for events,questions for an HCP, and the like. Generally the term “pattern” relatesto a repeating data arrangement identified in received data, e.g., inglucose monitoring, an occurrence of “overnight lows” that commonlyoccurs with the user.

The health characteristics and other characteristics can be gatheredusing a host monitoring system that incorporates a computing device,such as a smart phone, and one or more sensors, such a continuousglucose sensor, heart-rate monitor, GPS device, etc. Additionally, ahost can manually input information into the computing device or thedevice can automatically detect or receive inputs, such as mealinformation, medication administration times and amounts (e.g., from aninsulin delivery device), and the like. The information gathered by thehost monitoring system can be dynamically reported to the patient or canbe transmitted to one or more monitors used by caregivers. Thecaregiver(s) can then view and print dynamic reports about the host'shealth condition.

For purposes of illustration only, the following example is anon-limiting exemplary environment in which implementations of remotemonitoring systems described herein can be used.

In this exemplary environment, a host having diabetes is monitored byseveral different caregivers. The host has a continuous glucosemonitoring system, such as the DexCom G4® Platinum continuous glucosemonitoring system, commercially available from DexCom, Inc., whichprovides measurements of the host's glucose levels on a display device,such as the DexCom G4® Platinum Receiver, also commercially availablefrom DexCom, Inc.

Further, in this exemplary environment, the display device can be incommunication with a gateway device, either via wired communication orwireless communication. The gateway device gathers information,including real-time or near-real-time glucose concentration values, fromthe display device and transmits the information to a secure server. Thegateway device can include a smartphone, such as an iPhone 4S or iPhone5, each commercially available from Apple, Inc., and a host monitoringsoftware application that comprises instructions configured to cause thesmartphone to function as the gateway. The host monitoring softwareapplication can be in the form of a so-called “App” downloaded from theApple App Store operated by Apple, Inc. The gateway can transmitinformation gathered from the continuous glucose monitoring systemwirelessly to the secure server over a cellular network, Wi-Fi network,and the like.

The server can store and monitor the information received from themonitoring system. The monitoring can include comparing glucose valuesof the host (generated by the continuous glucose monitoring system andtransmitted to the server via the gateway) to predetermined thresholdsand initiating an action if a threshold is exceeded. For example, theserver can compare a current glucose value with a predetermined glucosethreshold and initiate a notification, such as a text message over acellular network, to a monitoring system of an HCP, caregiver, and/orpatient, if the glucose value exceeds the threshold. The server can alsoprovide historical and current glucose values to the monitoring systemon demand.

The following provides more detail of specific implementations, whichmay or may not include features noted in the above-discussed exemplaryenvironment.

FIG. 1 depicts a high-level system architecture of an implementation ofa monitoring system 100. Here, remote monitoring system 100 includes aplurality of host monitoring systems 112-116 connected to a plurality ofcaregiver (or patient, host, or user) monitors 104-108 via network 118.Each host monitoring system may be one or more health monitoring devicesthat gather health-related data, e.g., CGM or BG data, data aboutinsulin or the like, or other data, associated with a host and transmitthe health-related data via network 118. Exemplary implementations ofhost monitoring systems 112-116 are described in more detail elsewherein this disclosure, but in some implementations can include one or moresensors and computing devices operably coupled to the sensors to gather,process and transmit the health-related data. Network 118 can includeany communication medium, such as wired and wireless networks includingcellular networks, local area networks, wide area networks, Wi-Finetworks, the internet, and the like. Network 118 can also include oneor more servers 102 to process the health-related data received from andtransmit notifications and data to one or more caregiver monitors104-108 either automatically or in response to a request from thecaregiver monitors. Each caregiver monitor 104-108 can be associatedwith an individual or entity that is monitoring the health of one ormore of hosts using host monitoring systems 112-116. Each caregivermonitor can be associated with a caregiver, such as parent, spouse,doctor, nurse, hospital and the like, or the patient. The caregivermonitor can include a computing device that receives notifications fromnetwork 118 and requests additional information, such as historicalhealth related data generated by one or more host monitoring systems112-116.

Remote monitoring system 100 of FIG. 1 can also include workstation 110.Workstation 110 may be a computing device, such as a personal computer,that has access to remote monitoring system 100 for configuring settingsof system 100 and/or viewing information associated with one or morehost monitoring systems 112-116, such as reports dynamically generatedby caregiver monitoring systems based on a host's health-related data.Dynamically generated reports may be viewed or printed at a doctor'soffice, at the patient's home or displayed on a patient's computingdevice, e.g., smart phone or computer, or the like.

Using the monitoring system 100 of FIG. 1, one or more monitors 104-108can monitor one or more host monitoring systems 112-116. That is, hostmonitoring system 112 can be monitored by monitors 104 and 106, and atthe same time, monitor 104 can monitor host monitoring system 114 inaddition to host monitoring system 112. Various permissions andinvitations can be used to limit which caregiver monitors can monitorhost monitoring systems.

In one non-limiting example of remote monitoring system 100, each hostmonitoring system 112-116 comprises a smartphone, such as an iPhone fromApple, Inc., and, likewise, each caregiver monitor 104-108 has acomputing device such as a tablet computer, laptop computer, or desktopcomputer, or a smart mobile telephone, such as an iPhone. Each hostmobile telephone has a host software application downloaded from aserver of network 118, the application configuring the mobile telephoneto perform any of the functions by host monitoring system 112-116described herein, including gathering and transmitting health-relateddata used in remote monitoring system 100. The host software applicationcan be an application downloaded using the App Store service hosted byApple, Inc. Similarly, each caregiver monitor 104-108 has a remotemonitoring application downloaded from a server of network 118, themonitoring application configuring to perform any of the monitoringfunctions including the generation of dynamic reports described herein,including receiving notifications and requesting health-related data ofa host. The monitoring application can also be a software applicationdownloaded using the App Store service hosted by Apple, Inc.

FIG. 2A is a functional block diagram of an exemplary reporting anddisplay system 150 according to present principles. It will be notedthat the below described blocks may each constitute a number of pages orportals or subportals, or may further be combined into single pages.That is, the above and below described functional blocks may beimplemented as pages in a website or application, portals or subportalsto access a webpage or to enter data on a form, links to access webpagesor forms, or even as individual buttons on a page. Such are describedbelow in the context of particular implementations, such as blocks,pages, portals, subportals, views, and buttons, but such description isintended to encompass any implementation or combination ofimplementations, as appropriate and/or desired by the implementer.

Referring back to FIG. 2A, an access portal 120 may provide an initiallanding point for access of the dynamic reporting system. A user maythen choose to enter a patient sign-up/set up page 122 or a page 126 forHCP sign-up and set up. In some cases, the access portal 120 is notrequired and each set up page (for users/patients or HCPs) has its ownlanding page.

If a user or patient is entering the system, the patient sign-up/set upoption will be chosen and the patient may be provided with variousoptions to sign-up to the system, edit their profile, set uppreferences, or the like. Once such are set up, a page for a patientview 124 may be displayed as a dynamically generated or created report.If the patient has previously entered the system, he or she may be giventhe option to edit their profile or to confirm their information.Alternatively, upon log in, the patient may be directed to the patientview immediately, with a capability to edit their profile as a menuoption. A caregiver for a particular patient may generally use the page122 to enter the system and review dynamic reports for their subjectpatient. In the case where the caregiver monitors multiple patients,e.g., a parent of two or more children with diabetes, the caregiver mayenter the system through a portal somewhat akin to the HCP portal,enabling viewing and selection of multiple patients.

In many cases the patient view will mirror information provided in theHCP view for an individual patient, although in other cases the HCP viewwill include additional information primarily useful to the HCP.However, the customizability and dynamic nature of the systems andmethods according to present principles will generally allow the patientview to provide equivalent amounts of information, if desired by thepatient.

Referring to the right side of FIG. 2A, a similar capability may beprovided by page 126 for HCP sign-up and set up. This side of the systemwill generally have additional functionality, along with individualpatient dynamically generated or created reports, and HCPs may generallybe enabled to view multiple patients and in many cases all of thepatients treated by one or more clinics. Thus, upon HCP login, the HCPmay be enabled to manage multiple patients via a multi-patientmanagement page 128. The multi-patient management page 128 may allow anumber of operations related to patient management, including summaryviews of individual patients, the ability to “drill down” to a givenpatient's more detailed information, overall (cumulative or group)patient disease management, trends, patterns, and the like.

These aspects are described in greater detail in FIG. 2B, in which themore detailed system 150′ includes the patient sign-up and set up page122 and the HCP sign-up and set up page 126, as well as themulti-patient management page 128. These pages are situated as part ofthe portal 120 along with a login page 133 and a trial version page 131allowing new users to download and/or try out the reporting system.

Upon login by a patient or HCP, access is gained to the digital platform123 which provides a number of functional blocks as shown. These blocksare generally described below, and then a number of examples are givenfor the use of such blocks, both individually and in combination. Byanalysis of login information, systems and methods according to presentprinciples may determine the identity of the user logging in, and theidentity may further serve as a basis for dynamic generation of areport.

An aspect of systems and methods according to present principles is thedynamic generation of reports, and these reports can be displayed on thescreen of a computing device such as a computer or smart phone, and mayalso be printed for placement in a patient file and/or to guide adoctor-patient conversation. Accordingly, a printed reports system 125is provided to facilitate the generation of exemplary reports 153 a, 153b, and 153 c, which may flow from various types of reports in thedigital platform 123. The reports 153 a, 153 b, and 153 are exemplaryand generally may represent different report formats, that arepredefined or which may be configured by the user according to theirdesired report style. Exemplary print settings and reports are describedbelow with respect to FIGS. 33-44. The digital platform 123 includes anumber of blocks representing links or subportals to different portionsof the dynamically created report. An overview block 137 is provided asa quick summary of a patient's condition, and the same identifiesinformation at a high level to provide a summary of the patient'scurrent status. The same can also be employed to provide a summary ofthe patient's status over a given time frame, which can be a defaulttimeframe, but which can also dynamically vary depending on the amountof data, e.g., historical data, available. The overview block 137 canprovide an identification of patterns detected, but generally at a highlevel, representing the most significant patterns. Additional detailabout particular patterns may generally be provided by a patterns block139. The significant patterns identified and portrayed in the overviewblock 137 may be ranked or prioritized, as well as selected, based on anumber of factors, including available data. A ranking orprioritization, as well as the selection, may also be based onpredetermined criteria. In particular, identified patterns may becompared to criteria that have been determined as being pertinent,either to all patients or to that given patient. The criteria forpertinence of a given pattern may be set by the patient, by a caregiver,by an HCP, or may be provided by default (which may in turn beeditable). For example, in the context of glucose monitoring, if thepatient often encounters overnight lows, detection of a pattern of suchmay be highlighted in the overview subportal 137 not only due to therelevance to the user but also because of the overall importance of sucha pattern in diabetes management, as such patterns are generally notedas being particularly dangerous to users. Patterns may be selected andprioritized in a number of other ways as well. For example, patterns maybe selected based on their predominance to a user, e.g., if they are thedominant pattern the user encounters. In some cases patterns are shownif they are of particular educational value to a user, or particularlyexemplify a common effect, e.g., a rebound high, where a user's attemptto remedy a low results in a large upward excursion. The dynamicreporting system can also vary the pattern shown to a user so that userinterest in such reports is maintained. For example, while particularlydangerous patterns would likely always be shown to a user, one thatindicates a smaller area for improvement may be configured to onlyappear every other week, so that the user is reminded of the potentialarea for improvement, but the same does not unduly occupy screen orpaper “real estate” that could instead be employed for demonstratingother patterns. While of course all patterns could be shown, byselecting certain ones and prioritizing them, the user's attention isdrawn to the noted reports without causing fatigue or frustration in theuser.

The overview subportal or page 137 may also dynamically change over timebased on a number of factors. For example, as additional data isreceived, the data in fields or visualizations portrayed on the overviewpage may change to reflect the additional data. Similarly, the data infields or visualizations derivable from the received data may change assuch additional data is received and as such populate fields orvisualizations in the overview page.

The availability of data is not the only aspect that can result indynamic changes to the reports provided on or by the overview page 137.Additional data may lead to additional patterns being recognized, aswell as the recognition of events that may have occurred resulting fromthe new or updated additional data. Additional data may constitute newlyreceived sensor monitoring data, but may also constitute additional dataentered by a patient, additional data received from other sensors, e.g.,exercise data from accelerometers or GPS data, comments entered by thepatient, caregiver, or HCP, data from other medical devices such asmedicament pumps, data caused by changes in device settings, and thelike. In other cases, the patterns and overview page 137 may also changebased on less data being available. For example, the user may no longeruse a particular device, or the device may not be providing all the datait previously did due to a malfunction. In yet other cases, dataprovided by a first device may be supplanted by other data if the otherdata is “richer” than the data from the first device, e.g., providesmore information, is better calibrated, is more useful to the user, orthe like. Such a situation may occur when a user adds a CGM tomonitoring routine, where they previously used only an SMBG.

In any case, the dynamic reporting may, e.g., automatically adjust andaccount for cases where the user employs a CGM and pump in theirdiabetes management, or where just a CGM is employed (e.g., where thepump is defective), or even where just a pump is employed. It will beunderstood that the above discussion is only exemplary and that numerousvariations are possible given this teaching.

These aspects are described in greater detail below, but the flowchart175 of FIG. 2C illustrates at a high level one implementation of amethod for dynamic report generation.

In particular, as illustrated in the example of FIG. 2C, a step ofdynamic report generation 155 can be performed in at least two ways, oneway illustrated by the left side path and another way illustrated by theright side path.

On the left side path, a default report template may be generated (step157) based on user and system settings. The default report template mayinclude a number of data fields, e.g., glucose measurement value, and anumber of data visualizations, e.g., graphical representations ofpatterns, textual indications of steps to take in response to detectedpatterns, and the like. The default report template may then be modified(step 159) to reflect available data, user preferences, rankings orprioritizations of data fields and data visualizations, and the like.The modified default report template may then be populated with the data(step 161) and displayed to the patient or other user (or printed,etc.). Thus, on the left side path, a default template is created andthen modified.

On the right side path, a default template is not created or employed,but rather the report is generated sui generis based on available dataand prioritizations/rankings (and other factors as disclosed). In afirst step, data is analyzed to determine what is available and whatdata or other parameters are derivable from the available data (step163). In a next step, it is determined what data fields and datavisualizations can be constructed (step 165). This step may also includeperforming prioritizations or rankings as appropriate given user/HCPsettings. The dynamic report may then be generated according to thedetermined data fields and visualizations and prioritization/rankings(step 167).

The above ways of generating dynamic reports are exemplary, and it willbe understood that other ways are also possible, including ways thatcombine the left side path and the right side path.

Referring back to FIG. 2B, selection of a pattern by a user, orselection or activation of a “PATTERNS” button, leads the patient to apatterns block or subportal 139. The patterns block or subportalconstitutes one or more pages in which charts may be providedindicating, graphically or textually, patterns that have been detectedor identified. To illustrate, patterns can be detected by analyzing asignal trace over time, including comparison with the criteria, e.g.,previously identified signal patterns, but the same may also berecognized on a more relative basis, such as patterns detected aftermeals, patterns detected after exercise, or the like.

Generally the pattern data is a chart illustrating one or more traces(representations of detected signal values) over a timeframe includingthe detected pattern and a selected amount of time preceding thedetected pattern to illustrate provide context as to what factors mayhave caused or contributed to the detected pattern. Traces can sometimesbe portrayed group together by variability bars, which are similar toerror bars but generally reflect the variability of the response of abiological system and not an “error” per se. Patterns may be illustratedwith a single day's chart being portrayed as an example, or in a chartthat represents multiple days' data, using variability bars, multipletraces, or the like. Patterns may also be illustrated with a pluralityof single day charts that exemplify the pattern. In a multi-day view,individual days (or other time frames) may be portrayed as thumbnailswhich upon selection or activation causes display of a correspondingsingle day chart. Examples of these types are described below.

In systems and methods according to present principles, a dynamic reportmay be generated so as to illustrate the pattern to the patient or otheruser in the best way, but without inundating the patient or other userwith too much data, e.g., too many views, making it difficult to analyzethe pattern. In some cases, traces may be shifted in time so as tobetter illustrate patterns. For example, if a high-value of a traceoccurs 30 min. after breakfast, but the time of breakfast may shift fromday-to-day, the trace may shift by the corresponding amount, to indicatemore effectively the repeating aspect, that a high occurs 30 min. afterbreakfast.

Pattern reports may also be dynamically generated based on the amount ofdata available. In the above example, if a default template wouldotherwise show a pattern of “high after breakfast” and would showmultiple single day charts over the course of Monday-Friday illustratingthe pattern, if no data is available for Thursday, then rather than showa blank chart on Thursday, the Thursday chart may be dynamicallyremoved. As noted above with respect to data fields and visualizationsin the overview block, patterns displayed in this view may beautomatically updated as additional data becomes available or asadditional patterns are identified. Other dynamic changes ormodifications will also be understood. For example, the data and, e.g.,patterns portrayed may form a “sliding window”, and a user may slide thewindow to various other time frames (or expand or contract the window)to investigate and/or examine patterns occurring within these other timeframes. It is further noted that additional data input may lead to adynamic updating of a report, and the same may be especially commonwhen, upon identification of a pattern, a user inputs event data thatprovides an aspect of causation for a pattern, e.g., a user may entermeal data to explain a pattern of high glucose readings. In this casethe report may be dynamically updated in real time to reflect the addeddata.

By use of the pattern view, users may be enabled to identify events thatpreceded a pattern, e.g., by recognizing a regular pattern of activitythat occurred relative to a pattern of signal traces.

In the case of glucose monitoring patterns, data may be available from aCGM, and data may also be available, either as measured data or ascalibration data, from SMBG measurements. In another example of dynamicreport generation based on data available, if only one or the other typeof data is available, then only that data may be reported. For example,an SMBG chart may be employed when only SMBG data is available, and thesame for CGM data. If both are available, both may be reported. If CGMdata, e.g., a first set of data measured using a first technique, isavailable with blood glucose calibration data, e.g., a second set ofdata using a second technique, then both may be reported, and in somecases on the same chart. In some cases, blood glucose data that is notcalibration data may further be displayed on the same chart, suchconstituting a third set of data. In this case, the blood glucose dataused for calibration may be displayed in a different format than theblood glucose data simply displayed for its measurement value. Inpractical use, if both CGM and SMBG data are available, it may also becommon to display only the CGM data, due to its significantly greaterrichness. If data is available about insulin, e.g., via injection orpump, the same may be displayed as well, as may be carbohydrate or othermeal intake as well. Such patterns are illustrated below.

Within the patterns view, the user may also be enabled to selectivelyapply types of data to charts, as well as time frames over which data incharts are illustrated. A default may be provided and employed. Oneexemplary such default is that only data believed to be important to theparticular pattern is selected, but a user can modify such to allowother types or time frames of data to be displayed. Other defaults mayalso be employed, including generalized defaults. As one specificexample, where a pattern relates to overnight lows, a default may bethat meal and insulin data are displayed along with glucose data.

The patterns view may also generate helpful tips that could be used toimpact detected patterns. For example, one or more tips may be displayedbased on a pattern to either decrease the occurrence of the pattern (ifsuch is a deleterious pattern), or to increase the occurrence of thepattern (if such has been identified as helpful to the patient). As withthe pattern charts themselves, the tips may be dynamically created ordeleted based on a current status and available data, as well as ondevice and report settings.

Besides the pattern view, the digital platform 123 may also include adata block or view 141. While the pattern view is largely determinableby the reporting system, the data block allows users and HCPsconsiderable flexibility in analyzing the data, allowing “power users”the ability to “drill down” to significant levels of detail in data andperform detailed data analysis, for example.

For example, within the data block 141, a “COMPARE” feature may beprovided by which a user can compare two time frames of data, e.g.,signal traces, patterns, action items, suggested tips, comments, events,devices used, treatments, and/or the like. Where the user is comparingtraces, the traces may be over various time periods such as days, or maybe compared based on events (as determined by, e.g., detecting anappointment in a user electronic calendar). For example, a firsttimeframe can be a week prior to beginning a treatment, and the secondtimeframe can be a week after the beginning of the treatment. The secondtimeframe may also be a week prior to a doctor's appointment. Otherexemplary aspects to compare are described below, and still others willbe apparent to one of ordinary skill in the art given this teaching.

A questions block 145 may be provided within the digital platform 123which allows a user to input questions, which may later be reviewed bytheir clinician. In some cases, the clinician may be enabled to respondto the questions immediately, e.g., via e-mail or text, or within aresponse block (not shown) within the dynamic reporting system. Yetother questions may be stored for later discussion between the patientand the clinician during an appointment. A check box or other indicatorof completion may be employed by either the patient or the clinician toindicate that the matter has been discussed and the question adequatelyanswered. A clinician or other user may also use a questions block toinquire about disease management aspects of a patient, e.g., aboutbolusing medication or meal intake.

A settings block 147 may also be employed within the digital platform123 which allows the user, caregiver, and/or HCP to input and modifysettings associated with the patient. The settings block 147 may includeeditable data fields including user identifying information, clinicinformation, or the like.

The settings block 147 may also be employed to input target ranges,including a high predetermined threshold and a low predeterminedthreshold, as thresholds for hyperglycemic and hypoglycemic alerts oralarms, respectively. These thresholds may vary based on time of day,e.g., whether day or night, and/or by patient status, e.g., awake orsleeping. The settings block 147 may also be employed to link to analerts block 151 that allows a user to enter other alerts andnotifications settings, including, e.g., communications means tocaregivers (for text messaging alarms or the like). The block may alsobe employed to enter or modify other communication settings, includingcommunications with outside accounts, e.g., EMR accounts, socialnetworking accounts, or the like.

The settings block 147 may further be employed to indicate usual timesfor meals, e.g., breakfast, lunch, and dinner, and even within thesecommon meal items. In some cases, common meal items as input in thesettings block may in a reporting system be portrayed next tocheckboxes, and by a user checking the appropriate checkbox, data abouta meal may be entered into the system. By entering meal timinginformation into the settings block 147, identification of patterns dueto meal ingestion may be assisted.

The settings block 147 may also be employed to link to a devices block149 for a user to enter data about the devices they use for diseasemanagement, e.g., SMBGs, CGMs, pumps, or the like. Such device data maythen be portrayed in various dynamic reports as will be illustratedbelow. Additional details about settings are also described below.

FIG. 3 is a functional block diagram of a more detailed view of anexemplary reporting and display system 200 according to presentprinciples, as such may be particularly implemented in an HCPmulti-patient setting (although many features are also present in apatient view). The system 200 includes the digital platform 123 which isshown coupled to the multi-patient management block 128 which isdescribed in greater detail in FIG. 4. The digital platform 123 includesa link 138 to a history of action items, which may be implemented in adatabase, spreadsheet, or the like. Similarly, the platform may includea link 152 to a history of questions, which may be implementedsimilarly, as well as a link 154 to allow the scheduling of reminders orother calendaring functionality, as well as other doctor-patientcommunications.

The settings block 147 may include a profile 158 in which a user mayenter or edit identifying information, as well as a “LINK ACCOUNTS”block 166 in which patients may be linked with other patients,caregivers or HCPs, or vice versa. Utility functionality may be providedby a utility navigation bar 168. Within the bar 168, a link 170 may beprovided back to a list of patients. A link 172 may be provided toaccess billing/invoicing and payment information. A link 174 may beprovided to fetch additional information as needed, such as data fromother devices that are communicatively connected. Examples of such otherdevices may include, e.g., a pump, an SMBG meter, or the like. A link176 may be provided to export patient data, e.g., in a number offormats, e.g., CSV, as a particular type of database, and so on. A link125 may be provided to access the print functionality noted above anddescribed in greater detail below.

The print blocks indicated as elements 125 may pertain to either the HCPor the patient view. In general, it may be desirable in manyimplementations to have the print view be similar to an online, orscreen view, so that the patient, caregiver, or HCP need not learn adifferent set of conventions for one versus the other. Also in manyimplementations, it may be desirable to have the print view convey justas much information in black and white as in color, so as to enablethose without color printers to obtain needed information from a printedreport. In addition, black and white reports may be preferred by certainusers due to the higher expense of color printing. Modifications todefault print settings may be made easily using edit functionalityprovided within print block 125 or within a print settings block ormodule within setting block 147. The modifications may include differenttypes of data fields and data visualizations to be printed, but alsodifferent overall levels of data, e.g., a simpler printout or display ofonly key data fields and visualizations, or a complex printout of manydata fields and visualizations (perhaps prioritized in order ofimportance as perceived by the user, patient, caregiver, or HCP). In oneexemplary type of report, a one page dynamically generated summary isprinted. In another exemplary type of report, a comprehensive multipagedynamically generated report is provided, along with identified patternpages. In yet another exemplary type of report, a dynamically generatedsummary page is printed followed by the comprehensive multipage report.In yet another exemplary type of report, daily view options for both CGMand SMBG data may be provided. Other variations will also be understood.For example, and as will be illustrated below, print options may allowfor a section for a clinician's notes at the bottom of a page, suchbeing often identified as important for reimbursement as well as beingconvenient for HCPs that like to go over a printed version of the reportwith the patient. And as noted HCP-specific print settings and defaultsmay be employed to set a different default format for each HCP of apractice.

In general, reports may be viewed and printed by users, caregivers, andclinicians, in a number of ways and are generally controllable by theviewer. Such printable reports (which may also be convenient ways toview on-screen reports) are described below in greater detail withrespect to FIGS. 35-44.

A button 180 may be provided for logout. A footer bar 182 may beprovided to access, enter, and edit data such as: contact information184, health information 186, a privacy policy 188, and legal information190.

FIG. 4 illustrates a more detailed view of a multi-patient managementblock 128. The block 128 includes one or more subblocks including aperformance block 192, a patient list block 194, a questions block 210,as well as a clinic preferences block 212.

The performance block 192 is described in greater detail below but hereit is noted that the same can be effectively employed to monitor patientcompliance and other aspects of patient data and performance. Filtering,e.g., based on criteria, can also be employed to monitor the performanceand compliance of subgroups. In this way, the performance or complianceof a single patient may be monitored, as well as that of a group ofpatients, e.g., based on age, insurance, devices used, patient type(e.g., type I versus type II), how long a user has been a patient of theclinic, or the like.

The patients list block 194 may also include a filtering capability, andcan be sorted or arranged in various ways, e.g., patients may be sortedby appointment time, patients may be sorted and identified by theirtreatment needs, either by specific need or by urgency, patients may beidentified and sorted based on which are due for an appointment based oninsurance coverage, and so on. The patient list block 194 may furtherinclude one or more filtering blocks 196, an ADD PATIENT block 198, aPATIENT DETAILS block 202, an EXPORT block 204, a MERGE block 206, and aGENERATE INVITE CODE block 208. These blocks may be in many casesimplemented as buttons on the user interface.

In more detail, the filtering block 196 allows various filters to beapplied to the patient list to allow the clinician to focus on one ormore types of patients, e.g., on the basis of gender, age, weight, typesof devices used, or the like. The add patient block allows 198 patientsto be added into the system.

A patient details block 202 allows access to additional details about agiven patient, and in many cases leads to the overview and other viewsdescribed elsewhere in this specification. Upon selection of a patientto view via the patient details block 202, charts and other data aboutthe patient may be displayed. The same may also lead to a performanceview of the patient or patients.

The export block 204 allows exporting of patient data, e.g., for billingor EMR purposes or the like. The merge block 206 also allows patientdetails to be merged, e.g., such as within a family. The generate invitecode button 208 allows potential patients to be invited to become partof the patient group associated with a given position or clinic. A codeis generated which is sent to the potential patient, and upon acceptanceby the potential patient, the same may be monitored within a particulardynamic reporting system. The GENERATE INVITE CODE block may also beemployed to allow caregivers to become affiliated with one or morepatients.

The questions block 210 provides an overall list of questions which theclinician may then attempt to answer. These questions may be sorted orfiltered by patient, so that, e.g., if an appointment is upcoming, theclinician may attempt to have the questions answered by the time of theappointment. In so doing, in another implementation the questions block210 may enable the sorting of questions according to time of patientappointment.

The clinic preferences block 212 may have several sub blocks or buttonsassociated with the same, including a providers block 214, whichprovides access to the providers associated with the particularreporting system, as well as an ADD PROVIDER block 216, allowingaddition of new providers. An EXPORT DATA block 218 may be provided toallow export of provider and/or patient data. An ADMINISTRATIVE block220 may be employed to allow additional functionality, such as to manageusers via a users block 222, a remote scheduling functionality providedby REMOTE APPOINTMENT block 224, and a capability to customize softwarevia CUSTOMIZE SOFTWARE block 226. In more simple customization, logosfor a particular clinic may be added to the dynamically generatedreports. In more detailed customization, the customization may allowfunctionality not provided by base systems, and may be accomplished invarious programming languages, e.g., Visual Basic, JavaScript®, or thelike.

The clinic preferences block may also allow additional functionality,not shown. For example, a clinician may set what views to print inpreparation for patient appointments, and the same may even becustomized per patient. The clinician may enable particular settings forthe report, as well as default target glucose ranges they prefer fortheir patients.

The elements described above with respect to the functional blocks inthe digital platform are now illustrated below in reference to a numberof exemplary user interfaces. One exemplary user interface for the HCPview is shown in FIG. 5, and it will be noted that the patient view mayhave a similar format.

In the HCP setting, the overview block for a given patient may lead toan overview page 300 in which various aspects are displayed. First,identifying information 251 may be displayed for a patient as well assummary data 256 (described in greater detail below with respect to FIG.7). A navigation bar 257 may provide links to other page(s) as notedabove, including the (same) overview view 244, a patterns view 246, adata or analysis view 248, a questions view 252, and a settings view254. The patient identifying information, the summary data, and thenavigation bar may generally be consistent across views, although theparticular data fields and visualizations displayed will change withadditional and updated data.

The overview page 300 may also include dynamically generated patterndata 258, which patterns may be dynamically generated based on availabledata, prioritizations and rankings, or the like. In FIG. 5, patternsshown include overnight lows, lows after breakfast, weekend highs, highsafter dinner, as well as a patient's “best day” (which provides positivereinforcement by noting one or more or a pattern of days in which thepatient's disease management was under particularly good control). Thepattern data may be based on the time period selected by default or bythe patient, which time period is displayed in the timeframe bar 259.The timeframe bar 259 identifies the timeframe of data being analyzed,and can be modified to a different timeframe upon user choice. Adifferent timeframe would generally result in an overview page withdifferent data fields and data visualizations displayed. As noted abovethe time period may be selected by default or by the patient, and mayalso be determined based on available data. That is, if data is notavailable for a given time frame, that data may not be displayed (asopposed to, e.g., displaying a blank chart).

By analysis of data, patterns may be identified and selected (or not)for inclusion in the report. Patterns may also be prioritized or rankedsuch that they appear higher in the report. The selection and rankingmay be according to predetermined criteria. For example, criteria mayinclude a prioritized listing of patterns, prioritized by order ofseriousness. The patterns, if present, may then be dynamicallyprioritized according to the list. The list may also weight the patternsaccording to the degree of excursion outside of target range.

The display patterns may not only be areas for patient improvement, butcan also indicate patterns where the patient did well, providingpositive reinforcement for the patient, e.g., indicating their “bestday” or indicating an improvement such as the patient's glucose valuenot going high after a meal. Selecting a given pattern on the overviewpage 300 may cause the pattern view to open, showing the selectedpattern in additional detail.

The overview page 300 further illustrates a window 264 for action items.These action items may include specific steps or tips which have beenidentified by the system as being potentially of use in helping patientsmanage their disease. The overview block 137 may also be linked to adatabase or history of prior action items 138 (see FIG. 3). Such may beobtained in the user interface of FIG. 5 by selection or activation oflink 263. By providing convenient access to the history of action items,clinicians may be conveniently prompted to discuss patient status and tofollow-up with patients on such actions which had been previously setfor the patient.

The overview page 300 further illustrates a window 266 related todevices and their usage, as well as a window 268 for additionalinformation about devices and their usage. In this window can be listeddevices used within the selected timeframe and associated information,e.g., CGM's, BGs, insulin pumps, and the like. The device usage caninclude the average number of calibrations per day for devices that needto be or can be calibrated, such as a continuous glucose sensor.

In some implementations, reporting system 150 can also identify andreport if device usage guidelines have not been satisfied. For example,if a device is supposed to be calibrated twice a day per usageguidelines, the reporting software 150 can identify if data indicatesthe device was calibrated less than two times a day, and on which days.One way to do this is for the reporting software 150 identify how oftenthe device is supposed to be calibrated (e.g. once every 12 hours),analyte the data over the report timeframe for calibrations that complywith the requirements (with some acceptable margin, such as two or threehours), and flag and report any day in which the data indicates theusage does not comply.

FIG. 6 illustrates another exemplary overview page 350, but where onlySMBG data is available. The overall structure is similar to the CGMoverview page of FIG. 5, but many of the fields that may apply only whenCGM data is available are not provided in the SMBG view. However, itshould be noted that instead of creating a particular “BG view”, thesame is rather dynamically generated by consideration of the availabledata, which may be CGM, BG, or a combination. Where both are available,the dynamic reporting system and method according to present principlesmay give preference to CGM data, or to SMBG data, or may highlight oneversus the other. If only one has data available, that data may bedisplayed.

As with the CGM data, selection of particular features, such as patternsor action items may lead to additional information being displayed aboutthe selected feature. For example, if the pattern of overnight lows isselected, the displayed result may be one or more charts illustratingtraces occurring during nighttime in which the low signal value is seen.

FIG. 7 illustrates an exemplary summary section or window 256, whichincludes timeframe information, including a number of days 290 coveredby the summary as well as the specific dates spanned by the timeframe292. The summary section 256 can include a number of different displayedparameters of use to the patient, caregiver, or HCP.

In the specific exemplary implementation of FIG. 7, the summary section256 includes an estimated A1C value 294. Such estimated A1C values maybe calculated from algorithms using CGM data, SMBG data, as well as inother ways. This value provides an overall picture of diabetesmanagement over a period of 2 to 3 months. The summary can also providean indication of the variability 296, e.g., on a scale of 0 to 100,indicating textually and/or graphically how variable the patient'sglucose value has been over the given timeframe, a mean value of suchglucose level being illustrated by an element 298.

The summary section 256 may also include a target range element 302, thethreshold values of which being input by the patient, a caregiver, or anHCP, or provided by default. The target range element 302 may alsoinclude an indication of how often or how much time the patient has beenoutside of the target range, either generally or specifically, as shown,e.g., by a percentage above the range and a percentage below the range.The target range element 302 may further be displayed in a more granularway, if different portions of the day are provided with differentthreshold levels, e.g., where nighttime “low” thresholds are specifiedto be different from daytime thresholds. Generally thresholds can vary,including in an automatic periodic fashion, such as the noted dailyvariation where nighttime thresholds are different from daytimethresholds. Further, the thresholds can vary by manual changes, such asa user or healthcare practitioner changing one or more thresholds.Thresholds may also be provided with an initial default value or changedto a default value, where the default value is determined based on,e.g., user age, user insurance, user type of diabetes, user glucosecontrol metric, A1C value, user mean glucose value, or user mean glucosevariability.

The summary section 256 may also include an element 303 indicatingaverage daily insulin pump usage, e.g., by units of insulin, if suchinsulin pump data is available. Such data may be accompanied by otherrelated data, including average amounts of basal and bolus insulin 304,as well as percentage breakdown, and average number of daily boluses305. Also shown is an average daily number of glucose checks 306, wherethe “strips” visualization relates to the number of non-calibrationrelated SMBG checks performed on average per day (e.g., for dosing orinformational purposes), while the “calibration” visualization is theaverage number of calibrations of the CGM the user performed in a day.It is again noted that the above types of data are those measured withinthe selected timeframe, and that in each case the above noted data typesmay be provided by dynamic generation of the report, including omissionof such data types in the presentation and display of certain fields andvisualizations if such data types are unavailable, are underivable fromavailable data, or do not merit display if dictated by theprioritization or ranking scheme.

Additional data may be used in the calculation of elements within thesummary section, or may provide separate elements for displaythemselves.

FIG. 8 shows a similar summary section 256, but in the case where CGMdata is not available. In this case, e.g., the report dynamicallyadjusts to include SMBG data, or data derivable from such SMBG data, sothat the user can continue to receive some indication of their glucoseconcentration value. Many of the elements are the same as in FIG. 7, andtheir description is not repeated here. However, it is noted thissummary section 256 includes an element 316 pertaining to an averagenumber of daily glucose checks in the time period, which in this casediffers from the CGM summary section due to the discretization andnoncontinuous nature of the SMBG data. Moreover, the number of glucosechecks per day becomes clinically of significantly heightened importancewhen CGM data is not available. Thus, the visualization of FIG. 8dynamically changes relative to that of FIG. 7 due to the heightenedimportance of this number to the reviewer of the report.

FIG. 9 is a flowchart 500 illustrating an exemplary method according topresent principles. In a first step, data is received from one or moresources pertaining to the patient (step 320). This received data mightinclude receiving thresholds for what is determined to be hypoglycemiaor hyperglycemia for a given patient (step 322) and/or receiving datafor alert or alarm thresholds or about (step 324). These types of datamay be received from settings within the reporting system, including,e.g., one or more default settings for respective parameters such asthresholds, form data, a questionnaire provided to the patient,caregiver, or HCP, or the like. The receiving available data 320 mayfurther include receiving data about patient activity (step 326), e.g.,from an accelerometer or GPS. Certain types of patient activity data maybe gleaned from other sources as well, including time of day, e.g.,indicating waking activity versus sleeping. The receiving data mayfurther include receiving a glucose value from a sensor (step 328),e.g., CGM data, SMBG data, or the like. Such receiving data may furtherinclude calculating or deriving data from the received data, e.g.,slopes, accelerations, or the like. Such derived data can then beemployed in certain fields and visualizations. A user may further enterinformation, e.g., about exercise or about meals, e.g., data aboutcarbohydrate intake (step 330).

The data is then analyzed to determine results (step 332). The resultsmay include the detection of patterns (step 334), as well as adetermination of events, some or many of which may pertain to thepatterns detected (step 336). Such events may be detected and identifiedas being common to and/or preceding the repeating data arrangementsconstituting the detected patterns, e.g., having appeared in two or moredata arrangements, half the data arrangements, 75% of the dataarrangements, or the like. In this sense the term “common” is used torefer to appearing in more than one data arrangement constituting apattern, and not necessarily common to the user in general. Theprevalence of the event may be measured with reference to apredetermined ratio or percentage, such as appearing in at least 25% ofthe data arrangements constituting the pattern, 50%, 75%, 90%, 95%, or99%, and so on.

An icon may be displayed on the data visualization, at the timecorresponding to the event, to indicate the occurrence of the event. Auser-editable field may further be provided such that a user can enterinformation about the event, if known, so as to better inform thetreating healthcare provider. Other data about the event may also beindicated on the data visualization, e.g., the nature of the event, anaverage amount of time between the event and the start of the pattern,an effect of the event, or combinations of these. In the case where thedata corresponds to a glucose concentration value, the identifying theevent may include identifying increases or decreases in glucose valuepreceding two or more of the repeating data arrangements constitutingthe pattern.

Data may be analyzed to obtain other results, including comparisonresults as described below, such as to compare the effects of anattempted disease management modification.

The determined results may then be prioritized according to aprioritization or ranking scheme (step 338). The dynamic reportingsystem, having generated the report based on the available data, maythen display or print the prioritized determined results (step 342), ormay select a best or most illustrative result for display (step 344) orprinting. In another implementation, either instead of or in combinationwith the above steps, the reporting system may determine suggestions oraction items for the user based on the results (step 340), followed bydisplaying or printing the same. In some cases, a user may desireadditional information, e.g., the above noted “drilling down”, and soupon user request, the system may provide additional information (step346). For example, upon selection of a textual description of thepattern, the system may provide a series of daily charts showing thetrace data upon which the pattern was determined.

FIG. 10 is a more detailed flowchart 550 of a method according topresent principles, which, in particular, displays the left side of theflowchart of FIG. 2C. A first step is to receive available patient data(step 348). This step may be performed in the same way as step 320 ofFIG. 9. A default template is then received (step 352). The defaulttemplate indicates various data fields and data visualizations which maybe populated and displayed to a user, caregiver, or HCP. The defaulttemplate is then modified according to the received available data (step354). For example, data fields or visualizations may be removed or addedaccording to what data is available, as may time periods covering suchdata.

The template may then be populated with available data (step 356). Thisstep may include constructing the data visualizations, e.g., patterncharts, based on such data, as well as calculating data derivable fromthe received data for use in such data fields and visualizations (thisstep may also be performed prior to the populating step).

Besides modifying the default template, an optional step may beperformed of prioritizing the data fields and visualizations in thetemplate prior to display or printing (step 358). In this way, data ofmost interest to the user can be placed in a more prominent position,increasing the chance a user will pay attention to such data. This stepmay also include removing displayed data fields and data visualizationsthat have priority below a threshold. For example, depending on thelevel of detail desired by a user and set by the user in a settingsfield, a user may not desire to see patterns or pattern data which aredeemed of low importance. The prioritizing may be accomplished by userinput, by the use of user-defined criteria (step 362), or by the use ofdefault values set by the system (step 364).

The populated and prioritized template is then displayed or printed(step 360). In some cases, the priority of a particular data field orvisualization may be indicated by highlighting as well as by an order ofpresentation.

In yet another way, a dynamically generated report may be created by amethod shown, in one implementation, by a flowchart 600 of FIG. 11. Thismethod generally corresponds to the right side of FIG. 2C, and has asimilar first step as that of FIG. 10, namely the receiving of availableuser/patient data (step 366). In this case, however, a template is thencreated according to the received available data (step 368), and notnecessarily by use of a default template. The template is then populatedwith the available data (step 370). And in this case, generally, thedata fields and data visualizations will not require a “pruning” step asthe same have been determined for usage by the availability of the dataitself. As with FIG. 10, the fields and visualizations may beprioritized or ranked (step 372), using user input (step 374) or defaultvalues (step 376). The populated and/or prioritized template may then bedisplayed or printed (step 378).

FIG. 12 is a flowchart 650 according to another exemplary method, inwhich data fields and visualizations are not necessarily removed, butprioritization is employed to focus the user's attention on particularaspects in the dynamically generated report. Certain steps are similarto those disclosed above. For example, patient data is received (step380), as is a default template (step 382). According to user input, HCPinput, or default settings, or the like, the report is dynamicallygenerated by prioritizing the data fields and visualizations in thetemplate (step 384) so as to put particular focus or emphasis on thefields and visualizations according to the prioritization scheme. Thetemplate is then populated with available data (step 386), and thepopulated template is then displayed or printed (step 388) as adynamically generated report.

FIG. 13 is a flowchart 750 of a method which may be performed by an HCP,or in some cases by a caregiver, if the caregiver monitors multiplepatients. The method 750 is primarily for use when multiple patients orusers are being monitored. A first step is that the HCP logs onto thesystem (step 406). Upon selecting the performance option (step 408), thesystem may create and the HCP may view a dynamically generated reportshowing performance of all patients assigned to the HCP. The HCP mayalso filter the total list, so as to review a selected group (step 410).The filtering may be, e.g., on the basis of age, gender, device,insurance, type I versus type II, events, types of therapies, or thelike. The performance of the filtered group may then be displayed (step412). In some cases the performance of the group is displayed iscompared to a criteria, e.g., a control group. A compliance of the groupmay also be displayed (step 414). For example, the overall compliance ofthe filtered group to their desired action items, e.g., the complianceof the group in managing their disease, e.g., by use of A1C values, orthe like. Compliance of the patients in terms of meeting requirementslaid out by the patients' insurances may also be displayed.

Examples of specific reporting features are now described.

In a pattern view, also termed a pattern block, pattern window, patternsection, or the like, one or more patterns may be displayed to the useras detected by the system. The patterns shown may be exemplary andindicative of patterns described above, e.g., patterns 258 within theview 350 of FIG. 6, and may be accessed by activating the patterns block139 of FIG. 3. The system that detects the patterns (or otherwiseanalyzes data) may be the same as, or may be in data communication with,the reporting system. Referring to the pattern section 800 of FIG. 14, anumber of patterns 416 are illustrated.

The patterns may be dynamically generated such that the most importantpatterns are placed first, and the patterns are situated in order ofdecreasing importance. Each pattern may be illustrated by graphicaltrace data, in particular illustrated by thumbnails, as well as textualdata explaining the pattern. For example, the dynamic generation mayresult in a prioritization as described with respect to FIGS. 2C, 9, 10,11 and 12, and resulting in patterns as shown in FIGS. 5 and 6.

The first pattern shown is pattern 417 indicating that the patientexperienced a pattern of overnight lows. The pattern 417 is indicated ina thumbnail by illustrating three separate exemplary single day tracesshowing how the trace went below a threshold for a duration during theovernight time period.

Next shown as a pattern 419 illustrating that the patient experienced apattern of lows after breakfast. The pattern 419 not only illustratesthe low but also illustrates how thresholds may be configured to changedepending on daytime versus nighttime or on other bases. In this case,as well as in the other patterns below, only a single trace is shown,but the same may be the trace most illustrative of the pattern, or maybe a combination (e.g., an average) of traces illustrating the pattern,or the like. The intent is to indicate the pattern in a way so as todraw the user's attention to the same and provide a high-level visualexplanation of the detected pattern. In the pattern 419, the tracedescends below the lower threshold, and, as illustrated, the portionbetween the trace and the threshold may be filled in to indicate evenmore clearly to the user the drop.

The next pattern 421 indicates that the patient experienced a pattern ofweekend highs. The pattern 421 also indicates the use of changingthresholds, as seen by the higher hypoglycemic threshold 402 versus thelower hypoglycemic threshold 404, and indicates the pattern by traversalof the trace above the desired threshold in a number of instances, againwith the portion between the threshold and the trace filled in.

The next pattern 423 indicates that the patient experienced a pattern ofhighs after dinner. As with the above, filling in the portion betweenthe threshold and the trace may help indicate the pattern to the user.

The last chart shown is again of a single trace 425, which indicates thepatient's “best day”. The pattern 425 may be chosen based on a number offactors, e.g., which day the user had the longest duration without anexcursion outside of the lower or higher thresholds. In the case of thetrace 425, the patient was within the desired range for approximately 19hours. While the chart is shown as a single trace, patterns may also bedetermined from “best day” data. That is, where a patient follows aparticular routine, it may be expected that the “best day” will alsofollow a pattern.

It will be understood that other types of patterns may also be displayedand/or printed, besides those noted above. For example, patterns may beidentified or detected based on when highs or lows occur, e.g.,nighttime, morning, after certain events such as meals or exercise, orthe like. Patterns may also be identified or detected based on the dayof the week in which they occur, as many users have different routinesbased on weekdays versus weekends. Patterns may further be identified ordetected based on their relationship to events. Such events include theadministration of insulin, exercise, meals, or the like. Patterns mayalso be identified or detected based on combinations of one or more ofthe above factors. As noted it may be desirable to identify anddynamically report on patterns noted of good disease management. Such“good job” patterns may be employed to reinforce positive behavior, andfurther allows users to look back on such notifications of good diseasemanagement and try to repeat or emulate such behaviors. In thedetermination of such patterns, data may be analyzed for where patientsare within their predetermined range for significant durations, as wellas where patients reversed previously identified “bad” patterns.

FIG. 15 illustrates a dynamic report 850 in which a number of patterns418 are illustrated based on SMBG data values alone. The patterns shownare similar to those in FIG. 14, but are generally based on less datadue to the (non-continuous) nature of SMBG data. The report 850 includesa pattern of overnight lows 427, a pattern of lows after breakfast 429,a pattern of weekend highs 431, and the report of a best day 433.

FIG. 16 shows an illustrative action plan window 264. A number ofspecific suggested actions 394 a-394 e are illustrated, these based onthe data received, including patterns detected and other information,and results gleaned from data analysis. Checkboxes may be provided underthe action items to allow the user to indicate compliance with theaction item, as well as to convey to the user a feeling ofaccomplishment in finishing the task. A “SEE ALL” button 396 may also beprovided, which may lead to a window 950 shown in FIG. 17 indicating alist 422 of action items previously suggested. The list 422 can includea date of suggestion of the action item, a textual indication of theaction item, as well as an indication of completion.

FIG. 18 illustrates a greater detail of the devices and usage window266, which may form a portion of a dynamically generated report andwhich is a similar window as described above with respect to FIGS. 5 and6. The window 266 includes a list of devices 424 associated with theuser, which devices generally providing data which at least in partmakes up the dynamic report. If the devices interface with the reportingsystem, then generally, any data exposed by the device's API may be usedin the report. Such is often the case with pumps that may interface witha CGM. If the user enters data from another device, e.g., calibrationdata from an SMBG meter, then the data is limited to only that which theuser enters, and data derivable therefrom. In the list of devices 424,entries are given for a CGM, an SMBG meter, and a pump. Also shown areamounts of usage of the devices, e.g., how many hours per day theyprovide service to the patient, or in the case of the SMBG meter, howmany checks per day are made by the user. This portion of the report canalso be dynamically generated because data or data visualizations can beremoved, modified, highlighted, or the like, depending upon importance,or other criteria set by the system or a user.

FIG. 18 generally shows summary information for the devices and theirusage. On the other hand, FIG. 19 shows additional information which maybe displayed in a window 268 about one or more of the devices and theirusage. In particular, selecting one of the devices can cause expansionof that device's section to a window 268 that provides a significantamount of additional data about the device, in FIG. 19 the pump, as maybe available when the pump can interface with the reporting system. Suchadditional data may include pump identification information 437, pumpsettings 439, an insulin-to-carbohydrate ratio section 441, as well asstatistical information 443. While the expanded window illustratesadditional information for the pump, it will be understood thatselecting or activating the summary information for other devices willcause a similar expansion to show additional information associated withthe usage of that device.

In a dynamic reporting system according the present principles, the datawhich is not available may be omitted from the report, therebyclarifying the report and focusing the user's attention on fields andvisualizations for which data is available. If data for a particulardevice is not available for the selected timeframe, no data for thatdevice will be shown.

FIG. 20 shows another window 1050 which may be portrayed in a dynamicreporting system. In particular, the window 1050 shows a series 426 ofA1C data, organized by month, and in FIG. 20, spanning a date range fromDecember 2012 to November 2013. Both estimated and actual A1C values (asdetermined by an A1C test) may be schematically shown, where theestimated values are shown monthly and the actual values are shownquarterly. As above, if data is not available, portrayal of that data ina dynamically created report may be omitted, simplifying presentation ofthe report. Similarly, if data analysis indicates certain data is oflesser validity, the same may again be omitted or flagged as lessreliable. As may be seen, the window 1050 may have a button allowingentry of additional A1C data.

FIG. 21 illustrates another view or window 1100 within the pattern tabof navigation bar 257. In particular, this figure provides a wireframeview illustrating exemplary features. Other features may be portrayed invariations. In FIG. 21, each pattern identified may be listed as aseparate tab within pattern tab bar 445. The section may then listpertinent data about the particular pattern identified, including datafields and visualizations. The creation of such presented data may bedone in a dynamic fashion, as noted above, taking account of what datais available, as well as prioritization or ranking schemes.

One or more exemplary graphical data visualizations of the pattern maybe portrayed in a chart section 430 within the window 1100. Such datavisualizations may be as noted above and below, as a multi-day view, asa multi-day view using variability bars, as a series of single dayviews, or the like. Exemplary visualizations which may be portrayedwithin the window 1100 include those shown in FIGS. 22, 23, 24, 25, and27B.

Data available about events that preceded the pattern may be listed in asection 432. This information may include sections such as a food intakesection 458, an insulin on board section 460, an exercises section 462,a stress section 464, a medicines taken section 466, a health datasection 468, or the like. In some cases, the presented data will bemodified to only include available data. In another implementation, thesections 458, 460, 462, 464, 466, and 468 and the like may includeeditable text boxes allowing a user to enter comments and data aboutevents preceding the pattern. The presented data in section 432 may beprioritized according to that which is most likely to have had an effecton the pattern, e.g., post-meal highs may be particularly caused byprior food intake, and so on. Thus, the especially pertinent data may bedynamically placed first, highlighted, or otherwise called out.

As noted before, a questions section 434 may be provided for a user topose questions to their HCP about the detected pattern. And a suggestionsection 436 may be provided to indicate potential steps the user mayattempt to ameliorate or minimize the occurrence of the pattern in thefuture (or steps to repeat if the pattern is a good one). In some cases,if the user selects a suggestion in the suggestion section 436, the samemay be added to the action plan, such as seen on the overview window.

FIG. 22 shows a more advanced pattern chart 1150 which can form aportion of a dynamic report. The pattern chart 1150 can include atextual description 451 of the pattern, telling the user in plainlanguage about the detected pattern. A statistics section 447 may beprovided to indicate values such as the relevant time period, the meanglucose value, level of variability, and time within range compared totime outside of range. It will be understood that other statistics mayalso be provided in this section. Moreover, one or more of the valuescan be omitted, in a dynamically created report, if data is notavailable or is deemed less important (e.g., having a priority less thana threshold).

The chart may include a mean value trace 442 indicating the averagevalue of glucose over the noted time period, as a function of time, withthe timeframe dynamically selected to illustrate the detected pattern.The timeframe may also be selected to illustrate the pattern, as well asa time before and after the pattern, to assist in determining a cause ofthe pattern or to determine, e.g., how a patient rebounds from thepattern. For example, for lows experienced during nighttime, a timeframe may be selected including some time preceding the night timeframe.In this example, the time frame chosen may be selected to include dinnerso as to include the effect of meal ingestion on the nighttime low. Inone particular example, for a common event of a rebound high, a timeframe may be selected so as to visualize the low, the rebound high, aswell as subsequent user efforts to address the same.

An indicator 390 may be disposed on the chart showing varying or duallow threshold values, e.g., a daytime value and a nighttime value.Varying or multiple thresholds may be set for other reasons as well. Forexample, there could be multiple thresholds for low glucoseconcentration conditions and multiple thresholds for high glucoseconcentration conditions. Thresholds could be set (and varied) forregular meal times and/or regular exercise times. One or more thresholdscould also be set to dynamically and automatically adjust for givenconditions. For example, one or more set thresholds could be triggeredby certain events, i.e., upon the occurrence of an event, the thresholdis controlled to that value. For example, upon the occurrence of apattern being recognized for regular meal times or exercise, systems andmethods according to present principles may prompt the user to set oredit a threshold within the settings block or devices block, and in somecases may even suggest thresholds to set. Moreover, the threshold neednot be a straight horizontal line. Rather, the same may take the form ofthe curve and may reflect the biological processing of insulin orcarbohydrates within a host, e.g., the user, particularly ifpersonalized data such as insulin sensitivity are known to and modeledby the system and method. Again, the thresholds may be for alertingpurposes, alarming purposes, determining potential delivery of a bolus,and so on.

The thresholds may be set by the user in the settings block 147, as wellas in other ways. For example, the devices block 149 may have editablethreshold fields associated with each listed device, and in other casesusers may set thresholds using other blocks including patterns block139. Thresholds may also be set by being received from other devicesincluding SMBG meters and pumps. The indicator 390 may reflect one orall of these different thresholds. A slider bar 440 may be included toallow the user to view adjacent time periods, and zoom buttons may alsobe employed to expand or reduce the displayed timeframe.

While the mean value is indicated by trace 442, the overall pattern isindicated by the use of variability bars, indicating graphically howvalues varied above and below the mean. It should be noted thatindicating variability may be performed in a number of other ways aswell, including the use of an envelope surrounding the mean value, wherethe envelope represents a standard deviation, and so on. A portion 444indicates the duration of time when the patient was within the targetrange, a portion 446 indicates the duration of time when the user wasbelow the target range, and a portion 448 indicates the duration of timewhen the user was above the target range. As the pattern relates tohighs after dinner, a particular timeframe has been set apart byvertical bars, e.g., 8:25 PM to 12:10 AM, over which (on average) thehigh occurred.

The variability bars may change in color for portions of the bar thatfall within areas of the charts delimited by the thresholds (e.g., areasabove, between and below high and low thresholds), and the same is truefor signal traces in general, such as in FIGS. 14, 15, 23-25, 27B,35-37, and 39-47. Where the signal traces are displayed as discreteelements, the format of the elements may change above, between and belowgiven thresholds.

The chart 1150 may also include other information. In oneimplementation, the chart 1150 includes information about medicamentadministration. In the case of diabetes management, the medicamentadministration may include information about administration of insulin,e.g., pump usage, as shown by a horizontal bar having a heightindicating a basal rate 391 (which generally is constant, at least overshorter periods of time) as well as an indicator of boluses applied 452.Each bolus is indicated by an icon, and the number of icons can indicatethe number of boluses. By placing the icons indicating boluses at thislocation within the pattern chart 1150, i.e., above the graph, theboluses appear to “push down” on the glucose value, thus intuitivelyindicating to the user the effect of the insulin delivery. In the sameway, carbohydrate intake may be indicated by icons 454, and theirplacement below the chart may be so as to appear to “push up” on theglucose value, again, intuitively indicating to the user the effect ofcarbohydrate ingestion. As shown, a numerical indication may be providedto indicate the number of boluses or units of carbohydrates. In otherimplementations, described below in the context of FIGS. 42 and 46, theicon can have a particular shape, indicating a “tail”, giving greaterdetail on the effect over time of insulin or meals on a patient'sglucose value. In particular, the tail reflects as a function of time(and with an amplitude axis pointing downward, such that the “drip”shape of the tail represents positive values) the amount of activeinsulin in the user, also termed “insulin on board”, The amount ofinsulin on board may be determined algorithmically, and such algorithmsmay be in some cases “personalized” to the patient, where after time andhistory a given patient's insulin sensitivity is used to uniquelydetermine, or predict, the insulin on board and even the shape of thetail.

A navigation bar 449 can be placed adjacent the pattern chart so as toenable convenient navigation by the user to other patterns. A series oftoggles or other indications 393 may be displayed and employed toindicate to the user the types of data displayed on the chart.

FIG. 23 illustrates another form of chart 1175, and in particular a “dayview” chart, developed using data from the time shown in field 292 (atwo-week time period). This chart provides various information aboutpatterns, as will be described below in the context of the boldedvariability bars.

The chart 1175 may be displayed upon, e.g., a user selection of aparticular pattern within a list of pattern/pattern thumbnails, such asshown by patterns 416 of FIG. 14 or patterns 418 of FIG. 15. Selectionfrom the thumbnail or descriptive text may lead to a depiction like FIG.23, in which greater and additional detail about a pattern is provided.Such may be displayed on its own or in conjunction with the display ofother patterns. In one implementation, the chart 1175 occupies the placeof the wireframe place holder 430 in the view 1100. The pattern 416 maybe derived from a number of parameters, including the stored glucoseconcentration data, the selected time periods, data about devices andusage, and in some cases other data as may be available and which isrelevant to the selected time periods, such as user entered data, e.g.,about illnesses or subjective indications of how they feel, and otherlike data.

This chart includes certain elements as noted previously, and theirdescription is not repeated. The pattern chart 1175 also includesbuttons 395, which may toggle the display of the chart from indicating amulti-day pattern using variability bars versus indicating a multi-daypattern using a series of displayed single day charts. The user maychoose which style of chart, or the same may be automatically chosen bythe system depending on how best to illustrate the pattern. As with FIG.22, FIG. 23 employs variability bars. In addition to coloring, indicatedby crosshatching, specific horizontal lines are drawn showing the targetrange with upper and lower thresholds (the lower threshold 459 varyingaccording to daytime versus nighttime).

In FIG. 23, bolus information is indicated by a histogram 453, ratherthan the quantized bolus units shown in FIG. 22. The same is true ofcarbohydrate intake information 457. FIG. 23 also shows a varying basalrate 391′. The basal rate 391′ may vary based on a number of factors, akey one being that a user may have varied the rate in order to address aparticular event, e.g., a hyperglycemic event. The basal rate 391′ mayalso vary based on other factors. As one example, a user may program thesame to vary based on the time of day. Other reasons or causes forvaried rates will also be understood.

Variations are seen not only in the bar height, but also by numericalindicators. Variability bars are shown which may represent a range ofvalues encountered over a common time period, a standard deviation, orother statistical measure of variance of the measured glucoseconcentration value. Certain of the variability bars, and in particularthe bars 455, may be displayed which are bolded to indicate that apattern has been detected. For example, in FIG. 23, the bolded bars 455′in the midday range may indicate a lunchtime high, while the bolded bars455″ just prior to the evening range may represent an afternoon low.Other patterns will also be understood.

FIG. 24 illustrates a pattern chart 1225 in yet another form, in which aseries of single day traces 461 and 463 are portrayed one above theother. While the traces are separated by different days, each trace neednot represent an entire day, but rather a selected timeframe in order tobetter illustrate the pattern. Certain days are illustrated, generallythose determined to best illustrate the pattern over the analyzedtimeframe. However, other ways of choosing the illustrated days are alsopossible, including choosing days that illustrate a diversity of ways inwhich a pattern may manifest itself. For example, post-meal highs may beillustrated by a pattern occurring after dinners, a pattern occurringafter lunches, and so on.

In more detail, while various aspects are shown in the diagram relatingto other glucose excursions, a portion in which the noted pattern isseen, i.e., overnight lows, is dynamically highlighted in the trace 461as well as in the trace 463. For example, in the single day traces 461,the same is seen by a dot 471 (indicative of a single point SMBGmeasurement) near the beginning of the low period as well as in agraying out of the non-low portion 471′ of the trace 461. Similarhighlighting may be provided in traces for other patterns noted inpattern bar 445, e.g., in highs after dinner, or the like. In such away, a user can compare the different detected patterns, as well asevents which preceded each pattern. Such analysis is believed to providevaluable insight into diabetes management, and in particular to guidepatients, through the use of historical pattern data, to learn how tobetter manage their diabetes in the future.

FIG. 25 illustrates a pattern window 1300 in which a pattern chart 486is displayed dynamically created from just SMBG data. As with the CGMdata, a high threshold 490 and a low threshold 492 (the low thresholdvarying) are illustrated in the chart 486, along with a number of datapoints 488. Other elements are shown, which have been described above.The pattern is indicated by the textual description 451, and can be seenin the chart by a number of points appearing above the high threshold ata certain common period of time, e.g., during the day on a weekend. Suchmay be illustrated by dots having a common color with the highthreshold. A time period over which the highs occurred is dynamicallyindicated by displaying lines 494 and 496 between which the highoccurred.

Turning to data analysis, FIG. 26 illustrates an initial view 1350, inwire frame form, of a data portion of a dynamic report, the data portionbeing selected from the navigation bar 257. In general, the data viewallows a user to drill down into the data to obtain additionalinformation about the same, such as more detailed pattern data,information about devices and usage, and so on. The user may furthermodify time frames to obtain information about devices and eventsoccurring over various periods of time.

In the data view 1350, which has certain similarities to the patternview, a portion 498 may be reserved for data analysis as will bedescribed. As with the pattern view, a series of patterns may beprovided in a pattern window 502. A devices and usage section 506 may beprovided to indicate to a user the devices used in from which data mayhave been received in the data analysis and the creation of the dynamicreport. Using timeframe indicators 290 and 292, as described above, auser can select a particular time period and view data associated withthat time period. Such data may include fields as noted above includingpatterns for the selected timeframe, device usage over the time frame,comments entered by the user, and so on. A button 497 allows a user toview data associated with multiple time periods, such as for purposes ofcomparison.

In more detail, FIG. 27A illustrates the “compare” functionalityaccessed by the button 497 of FIG. 26. In this aspect, the exemplarydata analysis portion 498 is replaced with data from two time periods510 and 512, which are to be compared. The user can select the timeperiods using selection tools 290′, 292′, 290″, and 290″. The timeperiods shown can be “sliding windows” of time, and can thus be adjustedin duration and starting point (equivalently, ending point). While thecompared time periods are arbitrary, the user can select the same so asto illuminate the effects of various modifications or events. Forexample, the user may modify the time periods to illuminate the effectsof a medical intervention, attempted lifestyle changes, or the like. Forexample, the user may find it informative to compare the week/monthbefore a doctor's appointment and the week/month after, or the user maycompare a workweek versus a weekend, a week before and after a holiday,or successive weekends to gauge improvement, and so on. While in somecases the time periods compared are equal in duration, in other casesthe durations will vary.

In a specific implementation, when data is compared before and afterdoctor visits, the date of the doctor visit may be retrieved from thedoctor's calendar, e.g., as may be available from the multi-patientmanagement portal 128, and in particular from administration block 220.Such data may also be available from a user's calendar.

The systems and methods according to present principles may then, uponentry of a new time period or periods, automatically employ the existingdata, e.g., base glucose concentration data, the time period data, eventdata if any (either entered or previously recognized for the timeperiod), device usage data, user-entered data, etc., in a recalculationand potential re-recognition of patterns and other parameters, e.g.,derived variables, appropriate to the entered time periods. The systemsand methods may further be configured to, upon recognition of patterns,display the pattern and/or compared patterns as described above.

As can be seen, charts may be compared as may be patterns detectedduring the time frame encompassed by the different charts, and furtheras may be the devices and usage employed during the timeframeencompassed by the different charts. Various statistical data are alsoillustrated for comparison below each chart, and the same can include,e.g., estimated or actual A1C, variability, mean glucose value, targetrange, time in range, time out of range (high), time out of range (low),average insulin per day, average basal rate per day, average number ofboluses per day, a basal/bolus ratio, average carbohydrates per day, andso on. An exemplary comparison window 1425 is illustrated by the patterncharts 479 and 481 in FIG. 27B.

FIG. 27C shows additional detail about an exemplary comparison 1475 oftextual indications of pattern lists 487 and 489. As may be seen, thepattern of “highs after breakfast” in patterns list 487 is no longerpresent, as indicated by a strikethrough of element 491, in pattern list489. On the other hand, a new pattern 493 has been identified and thusadded to a pattern list 489, this pattern indicating “highs aftermeals”. The use of a strikethrough as applied to a noted pattern can beemployed to give helpful encouragement to a user, pointing out a clear“success” achieved by the user and recognized in a dynamic fashion bythe systems and methods according to present principles. The system (andthus, user) visualizes the pattern and also visualizes that the patternwas addressed and overcome. Of course systems and methods disclosed maysimilarly note the negation of good patterns as well as bad ones.

While the displayed time period or time frame can be selected bydefault, the same can also provide a useful tool for users, caregivers,and HCPs. For example, time periods can be selected based on an event,where the event is a doctor's appointment, an adjustment in therapy, orthe like, and the comparison tool used to determine the effect of thealtered situation on the patient's status.

FIG. 28 shows a “SETTINGS” landing page including a user profile (seealso FIG. 2B at blocks 147, 148, and 151; and FIG. 3 at the same blocksas well as blocks 149, 158, 162, and 166). Patient identifyinginformation, including e-mail address, username, and password, may beentered in section 514. The user may choose whether they wish to seeglucose values in mg/dL or mmol/L. Default or usual time periods formeals may be entered in section 516. Target ranges may be entered insection 518, including overall target ranges and target ranges aftermeals. Targets may also be independently set for different time periods,e.g., for the low threshold, e.g., a daytime value and a nighttimevalue. Devices employed by a user may be entered in section 520. Alertsand notifications may be set in section 522, and the same may includesettings for text messages or e-mails for weekly reminders, alerts oralarms for the user, as well as for caregivers or clinicians, and thelike. A section 524 may be provided to allow the user to link thereporting system with various accounts, including EMR accounts andsocial networks. Settings may be configurable by the user, a caregiver,or an HCP, as appropriate, with permissions given to certain users toedit certain fields.

Certain aspects particular to an HCP view were described above inconnection with FIG. 4. These aspects are described in greater detailbelow with respect to FIG. 29, which shows a view accessed uponselection of a multi-patient management block 128 (see FIGS. 2B and 4).First, activation or selection of a PERFORMANCE tab 546 on a navigationbar 526 may lead to performance view 1500. Other tabs may be provided,such as to access a patient list 548, a list of questions by patients552, and clinic preferences 554.

The performance view 1500 may have a number of aspects, including aninsurance compliance section 528, a total patient compliance section534, an indicator of a breakdown of patient type 536, an indicator ofpatients by age group 538, an indicator of patients by devices used 540,a list of newest patients 542, and an indicator of a number of data setsuploaded 544. It each case, selecting a field may serve to navigate theuser to a view containing more information associated with that field.

The insurance compliance section 528 may include a chart showing patientcompliance with various insurances as well as percentage breakdowns 530and 532 per insurance type. The total patient compliance may indicate,as a pie chart and/or by percentage, the number of patients that arecompliant versus non-compliant. The patient type section 536 mayindicate by a percentage the number of patients who are type I versustype II diabetes. The age group section 538 may indicate by a pie chartand percentages age ranges of the patients treated by a clinician orclinic. The devices section 540 may indicate by pie chart and percentagewhich patients are using particular devices, or combinations of devices.

The list of newest patients 542 may indicate the names of newly addedpatients, as well as one or more summary details about the same. Theindicator 544 may indicate new data sets added to the reporting system.Such data may alert the HCP that reports created for patients with newdata sets will generally be “new” in the sense that the dynamicreporting system will generally show new results.

In general, the performance view can be dynamically generated based onnew data from patients, as well as being based on a ranking orprioritization scheme instituted by a physician, or clinic. That is, theposition or clinic may edit settings, such that reports about multiplepatients are dynamically generated in a way that pertinent data, i.e.,especially important data, about the multiple patients is brought to theforefront, commanding the clinician's attention. Exemplary factors mayinclude, e.g., a degree to which diabetes is in control or out ofcontrol, calendaring data, e.g., all patients to be seen on a given day,a degree to which patients are compliant or noncompliant, a list ofpatients who have had significant glucose concentration valueexcursions, and so on.

Selecting or clicking on the patient list tab 548 generally brings up alist of patients 556, shown by the window 1550 of FIG. 30. The list 556may be sorted in a number of ways, including alphabetically. A searchfield may be provided to search for particular patients by name, or byinformation associated with a patient. In using search or otherfiltering techniques, the system may receive one or more criteriarelated to patient data, and compare the received criteria against datarecords in a database comprising a set of patient records. The systemmay then determine and display for viewing in the user interface one ormore patient records that meet the received criteria. The criteria mayinclude, e.g., age, weight, gender, insurance, length of time as apatient, type of malady, devices used to monitor or treat a malady,events associated with the patient, a therapy regime, criteria relatedto user malady treatment performance, or combinations of the above.Other criteria will also be understood.

The same techniques may also be employed to determine and display dataabout patient compliance, and in this case the criteria may be relatedto patient compliance with a therapy regime, e.g., accuracy of deviceusage, overall time of device usage, accuracy of calibrationmeasurements with respect to a suggested calibration time, number ofcalibrations, user acknowledgment of alarms, accuracy of medicamentadministration, or combinations of the above. Other criteria will alsobe understood.

The view 1550 may include the patient name, date of birth, device ID,the last time data was uploaded, appointment status, or the like. Aparticular patient may be highlighted, as shown by highlight 558. Inthis case, additional or expanded information may be displayed about thepatient in a sidebar 560. Additional information may include theirclinician, insurance information, whether they have questions pending,or the like. Within the sidebar 460, clicking on a VIEW PATIENT buttonmay lead to additional information being displayed about the patient,including a view in which only information about that selected patientis displayed, e.g., the overview view, pattern view, data view,questions/comments view, settings view, and the like, discussed herein.

Referring to the view 1600 of FIG. 31, a filter section 562 may bedisplayed and employed to examine a chosen group of patients. Byclicking on one of the radio buttons 563 i provided, the list ofpatients may be filtered to only include and display patients fittingthe criterion noted with the radio button. Particular radio buttons mayinclude, e.g., whether the patient uses a SMBG meter (563 a) or a CGM(563 b), whether the patient uses a pump (563 c), patient compliance,insurance, patient type (type I (563 d) versus type II (563 e)), agegroup, or the like.

Selecting or clicking on the questions tab 552 leads to a listing ofpending and/or answered questions, which the clinician may answer as agroup or may filter by patient to address a particular patient'squestions all at one time. Other variations of ways to organize theanswering of questions will also be understood.

Selecting or clicking on the clinic preferences tab 554 brings up (seeFIG. 32) a window 1650, which may list, among other items, clinicians564-568 associated with a given clinic, and their respectivepreferences. For example, each section may list a number of patientsassociated with the clinician, the particular print options therespective physician prefers, e.g., whether to include patterns,questions, comparisons, or the like. Clinicians may be enabled to selecta default data view, e.g., a number of days to be covered, a preferreddefault glucose target range for one or more patients, and so on.

To provide specific options for printed reports, a print options view1700 may be employed as shown by FIG. 33. The print options view 1700may include a date range for printout 571, a list of various defaultprint options 570 (shown in wire frame in FIGS. 33 and 34), and buttons572 to allow selective inclusion of various particular parameters. Thedefault print options may include any number of combinations of viewsdescribed. In the example of FIG. 33, the print options view 1700 canprovide specific options 570 for overview and devices, patterns, data,questions, and comparisons. The specific parameters in the embodiment ofFIG. 33, which may be selectively displayed in the dynamic report usingbuttons 572, include data about CGM, SMBG monitoring, carbohydrateingestion, exercise, health, insulin delivery, calibrations, alarmsettings, and notes. The result of inclusion of the data in a report maybe subject to the dynamic creation methods described above, e.g., basedon availability of data, prioritization/ranking schemes, and so on. Theuser may also be enabled to create their own print options, according totheir needs.

FIG. 34 shows a similar print options view, but the print options view1750 in this figure includes a calendar interface 574, by which the usercan conveniently select dates for inclusion in the dynamic report. Thecalendar interface 574 may indicate, e.g., by way of a star on aparticular date, the next appointment date with a clinician. If datawithin such the selected date range is not available, the same may beomitted or data fields and visualizations based on such may be placed ina lower priority, according to present principles.

FIG. 35 shows a printed dynamic report 1800 that may be particularlyuseful to guide a doctor-patient conversation. It is noted that suchreports, described here with respect to their printed form, may also bedisplayed on a GUI for similar purposes, with a similar such summarygraph 584. The data fields and visualizations within the report aredynamically generated to quickly focus on aspects important to thepatient and are further dynamically generated to exhibit in a concisemanner, e.g., one or a few pages, topics for discussion. A summarysection 580 is provided to give the clinician and patient an immediateexamination of the current status of the patient. A pattern section 582textually indicates patterns of relevance to the patient in a way notonly the clinician but also the patient can understand. The textualinformation further includes specific numbers, time frames,measurements, or the like, which gave rise to the identified pattern. Apattern chart 584 is provided to backup the identified patterns, and toindicate to a user in a graphical and quantitative way how the patternsare occurring, and such may accompany any of the patterns chartsdescribed herein, e.g., those in FIGS. 5, 6, 14, 15, 21-27, 35-37,39-41, 45, and 46. A notes section 586 may be employed by the clinicianto write or jot down notes during the doctor-patient conversation, whichmay later be stored in the patient file, either using the printed report1800 itself or a scanned-in version. In many cases, the same is helpfulif not required in requests for insurance reimbursement.

FIG. 36 shows a similar printed report 1850, but where a user selectedto only display SMBG data, (or where only SMBG data was available), andthus the dynamic reporting system only included the same. As with theCGM report, the SMBG report 1850 includes a summary section 588, apattern section 590, a chart section 592, and a notes section 596. Theprinted SMBG report 1850 may also include a daily aggregate of data 594,showing a scatter plot of data for each day, organized by day of theweek, within the time period. The patterns may be enumerated, andcorresponding numbers may appear in the chart or daily aggregate atlocations where such patterns may be particularly evident.

FIG. 37 shows another dynamically created or generated CGM report 1900,where a particular pattern has been “drilled down” to illustrateadditional information about, in this case, a pattern of overnight lows.For example, selecting this pattern in the displayed version of report1800 of FIG. 35 may cause the report 1900 to be generated. In this case,the pattern is illustrated by representative single day view examples604, 606, and 608. Above the single day views are certain statisticsabout the pattern, including a mean glucose level, a variability, anddurations within and outside of the target range.

FIG. 37 also shows a section 610 for comments, and in the case ofsection 610 the same prompt for comments by listing certain parametersthat may bear on the pattern, e.g., food, insulin, exercise, stress,medicine, and health. In this exemplary figure, the user “Marie”inserted comments about food and insulin, which the HCP may then employto determine more about the pattern and its causes, as well as potentialways to ameliorate such deleterious patterns in this patient.Suggestions are given in section 614, and the same may or may not bebased on the comments provided by the patient. A section 615 forclinician's notes is again provided, where the clinician may typicallyjot down notes about their conversation with the patient about thepattern or other aspects of disease management.

FIG. 38 shows a report 1925 in which information is provided aboutdevices and usage. In particular, a section 617 shows information abouta CGM, a section 619 shows information about a SMBG meter, and a section621 shows information about a pump. A number of other parameters aredisplayed about the pump, as may be available. It will be understoodthat the information provided in the devices and usage section may varyin a number of ways, and that the report 1925 is purely exemplary. Inmany cases, especially pertinent data may include the usage of eachdevice, i.e., how much or how often each device was used over the chosenor set time period of display. Such may generally be in the form ofhours (and fractions thereof) for CGM and pump data, and a number(absolute or average or as a frequency) for SMBG data. In someimplementations, data of a secondary priority may include devicesettings. Settings information may be employed to providerecommendations for the charts, such as, e.g., a recommendation toadjust a basal rate at a certain time, e.g., to lower the basal rate atnight. Device settings may be employed for the creation of charts aswell, e.g., the display of basal rate as in FIGS. 22-25.

FIG. 39 shows a printed version 1950 of a report similar to thatdisplayed in FIG. 22. Two larger charts 616 and 618 corresponding toidentified patterns are shown in considerable detail, and a series ofthumbnails 620 are illustrated for other detected patterns. The chart616 provides information about overnight lows; the chart 618 providesinformation about the identified pattern of highs after dinner;thumbnail 622 indicates lows after breakfast; thumbnail 624 indicateshighs after dinner; and thumbnail 626 indicates weekend highs. Byclicking on any of the thumbnails, the identified and selected patternmay be displayed in greater detail, such as in the larger charts abovethe thumbnails. The description of aspects of the charts themselves maybe seen with reference to the description of FIG. 22.

FIG. 40 illustrates a similar printed report 2150 including a chart 648,but where this chart further includes a number of calibration points(those marked with a ‘C’ within the point) as may be determined by SMBGmeasurements taken for purposes of CGM calibration.

FIG. 41 illustrates a report 2000 that is generally similar to FIG. 37,but where only SMBG data was selected to be displayed and/or analyzed(or was available), and thus the dynamic reporting system focused onsuch. In particular, a chart section 628 illustrates three single dayplots 630, 632, and 634, which illustrate a low, as evidenced by bloodglucose measurements taken within a common time period 631. The report2000 can include a section 636 for the patient to write notes about whatpreceded the pattern, as well as a section 638 in which notes, a userentered into the reporting system in attempts to address the pattern,are recorded and reproduced, e.g., for later discussion with an HCP.

Suggestions may be given to the user in section 640, and a section 642may be provided for a clinician to record notes, such as during adoctor-patient conversation.

Dynamically created reports may also be provided in other formats.Referring to FIG. 42, a printed report 2050 may display a plurality ofsingle day views 644, along a common time axis, indicating visually to auser, the occurrence of common patterns. For example, at point 633, apost-meal (lunch) high may be seen as a common pattern. Similarly, anafternoon low may be seen at point 635. Finally, a post-dinner high maybe seen at point 637.

FIG. 42 also shows how adjustments can be made to the icons, e.g., theirshape or size, to better reflect the effect of insulin or meals on thepatient, i.e., the effect of each over time, e.g., by the use of “tails”as described above and as illustrated in the figure as bolus tail 690. Adefault “effect” value can be employed until better calculations areachievable based on knowledge of a particular user's insulinsensitivity.

FIG. 43 illustrates a similar printed report 2100, but where only SMBGdata is available. The printed report 2100 includes a series of dailyviews 646. As with the SMBG data in FIG. 39, numerical designations mayaccompany data points so as to allow a visual indication of the value.Such may be particularly important when the report is printed on a blackand white printer, and thus where color cannot be used to indicate highor low values.

FIG. 44 illustrates another printed report for SMBG data, in which achart 654 includes an upper threshold 661 and a lower threshold 663, aswell as a number of points 655 within the target range, points 659 belowthe target range, and points 657 above the target range. As with priorfigures, stacked boluses 665 may be seen, and the same adjacent to abasal level to indicate the additive or cumulative nature of a bolusabove a basal rate. Carbohydrate ingestion or other meal intake isindicated by icons 667, which may be stacked according to their leveland amount.

In both CGM and SMBG data, stacking icons corresponding to boluses andmeal intake can help the user visualize the amount of each administered.In multi-day views of the same, stacking icons corresponding to bolusesand meal intake (or providing histogram views of the same) may beaccomplished by averaging the boluses and meal intake over the course ofthe multi-day time period. In single day views, the icons correspondingthe boluses and meal intake can represent the amounts actuallyadministered.

FIG. 45 shows a chart 656 within a report 2300 similar to that shown inFIG. 40, but where additional detail may be obtained in a displayedreport by hovering over one or more points or areas. For example,hovering over area 658 may provide additional information about a basalrate of insulin, e.g., a particular rate over a relevant time period.Hovering over an area 660 may provide additional information aboutboluses provided, e.g., an average number of boluses per day. Hoveringover area 662 may provide additional information about the variabilityindicated in the variability bars. Hovering over an area 664 may provideadditional information about the mean glucose trace value at a givenpoint in time. Hovering over an area 668 may provide additionalinformation about SMBG measurement values if several are clustered inthe hovered-over area. Hovering over an area 670 may provide additionalinformation about carbohydrate ingestion as evidenced by a set ofstacked icons.

FIG. 46 shows a chart 2350 within a dynamically created report, againwhere additional information may be obtained by hovering over one ormore points or areas. Hovering over an area 672 may provide additionalinformation about boluses, e.g., an amount delivered in a particularbolus delivery episode. Hovering over an area 674 may provide specificinformation about a SMBG calibration value measurement, the samepreviously only having been displayed via a point on the chart. Hoveringover an area 676 may provide a pop-up of a note written by a patient.Hovering over an area 678 may provide additional information about anindicated carbohydrate or meal intake, such as the time of day ofingestion, and amount ingested. Hovering over an area 680 may provideadditional information about a particular blood glucose measurement,e.g., a measurement value. Note that in this case, the SMBG measurementis being used as a particular data value and not as a calibration forCGM. FIG. 46 also shows a bolus data in the form of an icon 692 having atail as described above.

It is further noted that in all of the dynamic reports various eventscan be reflected in the charts by icons, and switches, such as radiobuttons, may be employed to turn such icons and portrayed events on andoff.

It is further noted that various charts may have a certain amount ofdata with which to populate data fields or create or generate datavisualizations, but that there may be a gap in the data, or an outlier.In these cases, attempts may be made to smooth the data, e.g., usinginterpolated or predicted values, to allow the data visualization tostill be dynamically generated. In some cases, using interpolated orpredicted values may be indicated on the chart by a dotted line or otherindicator to show that the same is not based (or only partially based)on actual data. Alternatively, a curve may simply be broken if the sameis missing more than a predetermined number of consecutive data points.Such a predetermined number can be based on a timeframe beingillustrated, e.g., for larger timeframes, a higher number of consecutivepoints can be missing. For shorter time frames, a lower number ofconsecutive points can be missing and still be portrayed in a datavisualization.

Further to gaps in data, it is recognized that it can be problematic todetect patterns in that that contain data gaps. In particular, howshould reporting system 150 treat data gaps when detecting patterns?Should the data gaps be considered as not contributing to a pattern, orassigned a value in some manner? In some implementations, reportingsystem 150 infers values of missing data, so as to be able to identifymore patterns. A particular implementation of reporting system 150 usesa two-step process for inferring values of data gaps, described below.

First, reporting system 150 identifies data gaps and determines if thedata gap is “span-able.” “Span-able” can mean whether enough data existsto infer sufficiently reliable values for that missing data. Whether agap is span-able can be based on a variety of criteria, including thenumber of missing data points, the reliability of the data pointsadjacent to the data gap, the clinical significance of the data pointsadjacent to the gap, and the like.

If the gap is determined to be span-able, then the reporting system 150infers data values for the missing data values. A variety of statisticalmethods can be used to infer the missing values. In one implementation,a cone of possibilities is used to infer the missing values. That is,the data before and after the gap of data are used, in addition to rateof change information, to infer the most likely values of the missingdata.

The inferred values of the missing data may then be used to detectpatterns, as discussed herein. Further, the values may be displayedeither as gaps or as values in reports. In some implementations, a usercan select a filter that either allows the display of inferred, missingdata points, or prevents the display of missing data points. Further,the missing, inferred data points can be visually displayed differentlythan non-missing data points, such as in a grey color instead of a blackcolor.

System 150 can also analyze the inferred data to determine if theinferred data may be clinically significant. For example, whetherinferred data changes a clinically significant detected pattern, system150 can decide to not use the inferred data, include a message to a userof the report that the detected pattern is based on inferred data and/ormodify the inferred data to provide a more conservative diagnosis. Insome implementations, the clinical significant determination can bebased on whether other possible inferred interpretations (e.g., using acone of possibilities analysis) of the missing data would change theclinical significance of a determination (e.g. detected pattern). As anon-limiting example, system 150 may, under a first inference using acone of possibilities analysis, infer data points in a missing-data gapthat result in no detected night-time low pattern, but, in a second,different inference using the cone of possibilities analysis, results inan identification of a reoccurring night-time low pattern. In such asituation, the system may use the more conservative inferred data thatresults in the detected night-time low pattern detection and/or includea message in the report that the missing data affected the patterndetection analysis.

Automatic Generation of Reports and Notifications

As can be appreciated, the above described reports can be extremelyvaluable to a patient or health care provider in managing a patient'sdiabetes or other health condition. However, a patient or healthcareprovider may not remember to view reports on a periodic basis, or beaware of a time when viewing a report could add particular value tomanaging the patient's condition. Accordingly, some implementations ofreporting system 150 automatically generate a report and/or notify auser (e.g., patient or health care provider) to view a report.

In some implementations, a user of reporting system 150 can use settings147 (FIG. 2B) to trigger reporting system 150 to send a notification(such as via SMS text message, email and the like) to user regarding areport. The report can be a report pre-designated by the user; forexample, a one-week or one-moth report. The reporting system 150 cantrigger the notification automatically based on a reoccurring timeframe(e.g., once every week, once every month, a time period before an event,such as a meal, doctor's appointment or calendared vacation), orautomatically based on reporting system 150 detecting one or morepatterns or other conditions based on the patient's data. Thenotification can comprise an electronic version of the designated report(e.g., a PDF) that the user can view, or can contain an electronic linkthat, when selected by a user, launches an application running on theuser's device causing the report to be displayed on the user's device.The notification can alternatively or additionally include a textualdescription describing a result of the report, such as “Congratulations,you have fewer detected nighttime lows this week than last week!”,should reporting system 150 detect such a situation.

As one non-limiting, illustrative example, a user can first generate areport using reporting system 150 comparing a current week's data to aprevious week's data. Reporting system 150 then allows the user, throughsettings 147, for example, or at the time of printing the report, asanother example, to select an option to set up automatic reoccurringnotifications. The automatic reoccurring notifications can further allowthe user to select a timeframe, such as once a week. If automatic,reoccurring notifications are enabled, then reporting systemautomatically notifies the user via the user's preferred notificationmethod (provided in settings 147) of the report. The notification canalso include an electronic version of a newly generated report comparingthe now current week's data to the now past week's date, which are bothnow one week later than the originally generated report discussedearlier.

As further illustrative example, a user can use reporting system 150 toset up a notification based on a detected pattern, such as nighttimelows. Here, reporting system 150 can automatically send a notificationto the user if a predetermined number of nighttime low events aredetected over a predetermined amount of time, such as two nighttime lowevents over a one week period. The notification can include a textualexplanation of the detected pattern as well as an electronic version ofa report illustrating the events or an electronic link to the report.

General Description of a Glucose Monitoring System

While the systems and methods according to present principles may beemployed within any number of health monitoring environments, oneparticular embodiment may be employed in glucose monitoring and thedynamic generation of reports therefore. Other types of analytes whichmay be monitored are listed elsewhere in this specification. Aparticular monitor system for the analyte of glucose is described below.

A glucose sensor may be employed that measures a concentration ofglucose or a substance indicative of the concentration or presence ofthe glucose. In some embodiments, the glucose sensor is a continuousdevice, for example a subcutaneous, transdermal, or intravasculardevice. In some embodiments, the device can analyze a plurality ofintermittent blood samples. The glucose sensor can use any method ofglucose measurement, including enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, radiometric, and the like.

The glucose sensor can use any known method, including invasive,minimally invasive, and non-invasive sensing techniques, to provide adata stream indicative of the concentration of glucose in a host. Thedata stream is typically a raw data signal that is transformed toprovide a useful value of glucose to a user, such as a patient orclinician, who may be using the sensor.

Glucose Sensor

The glucose sensor can be any device capable of measuring theconcentration of glucose. One exemplary embodiment is described below,which utilizes an implantable glucose sensor. However, it should beunderstood that the devices and methods described herein can be appliedto any device capable of detecting a concentration of glucose andproviding an output signal that represents the concentration of glucose.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of another analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, intraocularand/or intravascular (e.g., intravenous) device. In some embodiments,the device can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., clinician), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor capable of measuring the concentration of glucose in a host, thesystems and methods of embodiments can be applied to any measurableanalyte, a list of appropriate analytes noted above. Some exemplaryembodiments described below utilize an implantable glucose sensor.However, it should be understood that the devices and methods describedherein can be applied to any device capable of detecting a concentrationof analyte and providing an output signal that represents theconcentration of the analyte.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Patent Publication No.US-2006-0020187-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1, co-pendingU.S. Patent Publication No. 2008/0119703, U.S. Patent Publication No.2008/0108942, and co-pending U.S. Pat. No. 7,828,728. In one alternativeembodiment, the continuous glucose sensor comprises a transcutaneoussensor such as described in U.S. Pat. No. 6,565,509 to Say et al., forexample. In another alternative embodiment, the continuous glucosesensor comprises a subcutaneous sensor such as described with referenceto U.S. Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No.6,484,046 to Say et al., for example. In another alternative embodiment,the continuous glucose sensor comprises a refillable subcutaneous sensorsuch as described with reference to U.S. Pat. No. 6,512,939 to Colvin etal., for example. In another alternative embodiment, the continuousglucose sensor comprises an intravascular sensor such as described withreference to U.S. Pat. No. 6,477,395 to Schulman et al., for example. Inanother alternative embodiment, the continuous glucose sensor comprisesan intravascular sensor such as described with reference to U.S. Pat.No. 6,424,847 to Mastrototaro et al.

Details of sensors, sensor electronics, and signal receiver and displaycomponents may be as described in the applications incorporated byreference above, as well as in, e.g., US Patent Publication No.2013/0078912, incorporated by reference in its entirety.

Receiver/Monitor

FIG. 47 is a schematic view of a receiver or monitor 2550 includingrepresentations of estimated glucose values on its user interface. Themonitor 2550 comprises systems to receive, process, and display sensordata from the glucose sensor (e.g., 2450), such as described herein.Particularly, the monitor 2550 can be a mobile phone type device, forexample, and comprise a user interface that has a physical button 730and a display screen 732, as well as one or more input/output (I/O)devices, such as one or more buttons and/or switches, which whenactivated or clicked perform one or more functions. In the illustratedembodiment, the electronic device is a smartphone, and the display 732comprises a touchscreen, which also functions as an I/O device. In someembodiments, the user interface can also include a keyboard, a speaker,and a vibrator. The functions of the monitor or smart phone can also beimplemented as functions within an application running on a tabletcomputer, laptop computer, desktop computer, or like device. In otherembodiments, the receiver may comprise a device or devices other than asmartphone, such as a smartwatch, a tablet computer, a mini-tabletcomputer, a handheld personal digital assistant (PDA), a game console, amultimedia player, a wearable device, such as those described above, ascreen in an automobile or other vehicle, a dedicated receiver device,etc. In any case, the display screen of such computing environments maybe employed to display dynamically created reports, and the computingenvironment may further be employed to print the dynamic reports notedherein.

FIG. 48 is a block diagram that illustrates one possible configurationof the monitor electronics, e.g., a smart phone. It is noted that themonitor can comprise a configuration such as described with reference toFIG. 47, above. Alternatively, the monitor can comprise otherconfigurations, including a desktop computer, laptop computer, apersonal digital assistant (PDA), a server (local or remote to thereceiver), and the like. In some embodiments, the receiver can beadapted to connect (via wired or wireless connection) to a desktopcomputer, laptop computer, PDA, server (local or remote to thereceiver), and the like, in order to download data from the receiver. Insome alternative embodiments, the monitor and/or monitor electronics canbe housed within or directly connected to the sensor (e.g., 2450) in amanner that allows sensor and receiver electronics to work directlytogether and/or share data processing resources. Accordingly, thereceiver's electronics can be generally referred to as a “computersystem.”

A quartz crystal 734 is operatively connected to an RF transceiver 736that together function to receive and synchronize data streams (e.g.,raw data streams transmitted from the RF transceiver). Once received, aprocessor 738 processes the signals, such as described below.

The processor 738, also referred to as the processor module, is thecentral control unit that performs the processing, such as storing data,analyzing data streams, calibrating analyte sensor data, predictinganalyte values, comparing predicted analyte values with correspondingmeasured analyte values, analyzing a variation of predicted analytevalues, downloading data, and controlling the user interface, e.g.,generating dynamic reports, by providing analyte values, prompts,messages, warnings, alarms, data fields, data visualizations, and thelike. The processor includes hardware and software that performs theprocessing described herein, for example flash memory provides permanentor semi-permanent storage of data, storing data such as sensor ID,receiver ID, and programming to process data streams (for example,programming for performing prediction and other algorithms describedelsewhere herein) and random access memory (RAM) stores the system'scache memory and is helpful in data processing.

In one exemplary embodiment, the processor is a microprocessor thatprovides the processing, such as calibration algorithms stored within aROM 740. The ROM 740 is operatively connected to the processor 738 andprovides semi-permanent storage of data, storing data such as receiverID and programming to process data streams (e.g., programming forperforming calibration and other algorithms described elsewhere herein).In this exemplary embodiment, a RAM 742 is used for the system's cachememory and is helpful in data processing.

A battery 744 is operatively connected to the processor 738 and providespower for the receiver. In one embodiment, the battery is a standard AAAalkaline battery, however any appropriately sized and powered batterycan be used. In some embodiments, a plurality of batteries can be usedto power the system. A quartz crystal 746 is operatively connected tothe processor 738 and maintains system time for the computer system as awhole.

A user interface 754 comprises a keyboard 2, speaker 3, vibrator 4,backlight 5, liquid crystal display (LCD 6), and one or more buttons 7,which may be implemented as physical buttons or buttons on a touchscreeninterface. The components that comprise the user interface 754 providecontrols to interact with the user. The keyboard 2 can allow, forexample, input of user information about himself/herself, such asmealtime, exercise, insulin administration, and reference glucosevalues. The speaker 3 can provide, for example, audible signals oralerts for conditions such as present and/or predicted hyper- andhypoglycemic conditions. The vibrator 4 can provide, for example,tactile signals or alerts for reasons such as described with referenceto the speaker, above. The backlight 5 can be provided, for example, toaid the user in reading the LCD in low light conditions. The LCD 6 canbe provided, for example, to provide the user with visual data outputsuch as is illustrated in FIG. 50, or the data fields and datavisualizations described above. The buttons 7 can provide for toggle,menu selection, option selection, mode selection, and reset, forexample.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as requests forreference analyte values, therapy recommendations, deviation of themeasured analyte values from the predicted analyte values, and the like.Additionally, prompts can be displayed to guide the user throughcalibration or trouble-shooting of the calibration.

In some implementations, the continuous analyte sensor system includes aDexCom G4® Platinum glucose sensor and transmitter commerciallyavailable from DexCom, Inc., for continuously monitoring a host'sglucose levels.

In some embodiments, the system may execute various applications, forexample, a CGM application, which may be downloaded to the receiver orother electronic device over the Internet and/or a cellular network, andthe like. Data for various applications may be shared between the deviceand one or more other devices/systems, and stored by cloud or networkstorage and/or on one or more other devices/systems. The data so storedmay form the basis of the dynamic reports described above.

The connections between the elements shown in the figures illustrateexemplary communication paths. Additional communication paths, eitherdirect or via an intermediary, may be included to further facilitate theexchange of information between the elements. The communication pathsmay be bi-directional communication paths allowing the elements toexchange information.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure (such as the blocks of FIG.48) may be implemented or performed with a general purpose processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array signal (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise varioustypes of RAM, ROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray® disc wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects a computer readable mediummay comprise non-transitory computer readable medium (e.g., tangiblemedia). In addition, in some aspects a computer readable medium maycomprise transitory computer readable medium (e.g., a signal).Combinations of the above should also be included within the scope ofcomputer-readable media.

The methods disclosed herein comprise one or more steps or actions forachieving the described methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Certain aspects may comprise a computer program product for performingthe operations presented herein. For example, such a computer programproduct may comprise a computer readable medium having instructionsstored (and/or encoded) thereon, the instructions being executable byone or more processors to perform the operations described herein. Forcertain aspects, the computer program product may include packagingmaterial.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

The system and method may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out on computerreadable media, generally non-transitory, and these instructions aresufficient to allow a processor in the computing device to implement themethod. The computer readable medium may be a hard drive or solid statestorage having instructions that, when run, are loaded into randomaccess memory. Inputs to the application, e.g., from the plurality ofusers or from any one user, may be by any number of appropriate computerinput devices. For example, users may employ a keyboard, mouse,touchscreen, joystick, trackpad, other pointing device, or any othersuch computer input device to input data relevant to the calculations.Data may also be input by way of an inserted memory chip, hard drive,flash drives, flash memory, optical media, magnetic media, or any othertype of file—storing medium. The outputs may be delivered to a user byway of a video graphics card, graphics processor, or integrated graphicschipset coupled to a display that maybe seen by a user. Alternatively, aprinter may be employed to output hard copies of the results includingthe formats described above with respect to FIGS. 35-44. Given thisteaching, any number of other tangible outputs will also be understoodto be contemplated. For example, outputs may be stored on a memory chip,hard drive, flash drives, flash memory, optical media, magnetic media,or any other type of output. It should also be noted that the aspectsmay be implemented on any number of different types of computingdevices, e.g., personal computers, laptop computers, notebook computers,net book computers, handheld computers, personal digital assistants,mobile phones, smart phones, tablet computers, and also on devicesspecifically designed for these purpose. In one implementation, a userof a smart phone or wi-fi—connected device downloads a copy of theapplication to their device from a server using a wireless Internetconnection. An appropriate authentication procedure and securetransaction process may provide for payment to be made to the seller.The application may download over the mobile connection, or over theWiFi or other wireless network connection. The application may then berun by the user. Such a networked system may provide a suitablecomputing environment for an implementation in which a plurality ofusers provide separate inputs to the system and method. In the abovesystem where, for some implementations, multiple sources of data arecontemplated, the plural inputs may allow plural users to input relevantdata at the same time.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method of identifying and reporting eventspreceding a pattern in a set of user data, comprising: a. receiving aset of data about a user; b. identifying a pattern in the received data,the pattern representing repeating data arrangements in the receiveddata; c. displaying a data visualization corresponding to the identifiedpattern; d. identifying at least one event preceding one or more of therepeating data arrangements; and e. displaying an indication of theidentified event on the displayed data visualization.
 2. The method ofclaim 1, wherein the identifying at least one event further comprisesidentifying at least one event preceding at least a predeterminedpercentage of the repeating data arrangements.
 3. The method of claim 2,wherein the percentage is at least 50%.
 4. The method of claim 3,wherein the percentage is at least 75%.
 5. The method of claim 4,further comprising displaying an icon corresponding to the event on thedisplayed data visualization.
 6. The method of claim 1, furthercomprising displaying data in a window, frame, or layer, correspondingto the event, on or within the displayed data visualization.
 7. Themethod of claim 6, wherein the data corresponding to the event includesdata about a nature of the event, an average amount of time between theevent and a start of the pattern, or an effect of the event, orcombinations of the above.
 8. The method of claim 1, further comprisingdisplaying an editable field along with the indication of the identifiedevent, whereby a user can enter and store information about the event.9. The method of claim 1, wherein the identifying at least one eventfurther comprises comparing an event against one or more criteria todetermine if the event pertains to the identified pattern.
 10. Themethod of claim 1, wherein the data is a glucose concentration value,and wherein the identifying at least one event further comprisesidentifying at least one increase or decrease in glucose value precedingtwo or more of the repeating data arrangements.
 11. A system foridentifying and reporting events preceding a pattern in a set of userdata, comprising: a. an analyte sensor configured for receiving a set ofdata about a user; b. a processor configured for identifying a patternin the received data, the pattern representing repeating dataarrangements in the received data; c. a display configured fordisplaying a data visualization corresponding to the identified pattern;d. a processor configured for identifying at least one event precedingone or more of the repeating data arrangements; and e. a displayconfigured for displaying an indication of the identified event on thedisplayed data visualization.
 12. The system of claim 11, wherein theidentifying at least one event further comprises identifying at leastone event preceding at least a predetermined percentage of the repeatingdata arrangements.
 13. The system of claim 12, wherein the percentage isat least 50%.
 14. The system of claim 13, wherein the percentage is atleast 75%.
 15. The system of claim 14, further comprising a displayconfigured for displaying an icon corresponding to the event on thedisplayed data visualization.
 16. The system of claim 11, furthercomprising a display configured for displaying data in a window, frame,or layer, corresponding to the event, on or within the displayed datavisualization.
 17. The system of claim 16, wherein the datacorresponding to the event includes data about a nature of the event, anaverage amount of time between the event and a start of the pattern, oran effect of the event, or combinations of the above.
 18. The system ofclaim 11, further comprising a display configured for displaying aneditable field along with the indication of the identified event,whereby a user can enter and store information about the event.
 19. Thesystem of claim 11, wherein the identifying at least one event furthercomprises comparing an event against one or more criteria to determineif the event pertains to the identified pattern.
 20. The system of claim11, wherein the data is a glucose concentration value, and wherein theidentifying at least one event further comprises identifying at leastone increase or decrease in glucose value preceding two or more of therepeating data arrangements.